Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures, joint effusions and dislocations during the review of radiographs of the musculoskeletal system. Rayvolve is indicated for the adult and pediatric population (≥2 years). Rayvolve is indicated for radiographs of the following industry-standard radiographic views and study types. Rayvolve is indicated for use in adult and pediatric populations to help detect fracture, dislocations and joint effusions on osteoarticular X-rays of the following anatomic area of interest/radiographic views: Study type (Anatomic Area of interest) / Radiographic Views* supported for fracture detection: - Ankle / AP, Lateral, Oblique - Clavicle / AP, AP Angulated View - Elbow / AP, Lateral - Forearm / AP, Lateral - Hip / AP, Frog leg lateral - Humerus / AP, Lateral - Knee / AP, Lateral - Pelvis / AP - Shoulder / AP, Lateral, Axillary - Tibia/fibula / AP, Lateral - Wrist / PA, Lateral, Oblique -Hand / PA, Lateral, Oblique -Foot / AP, Lateral, Oblique. -Ribs AP -Spine PA, Lateral Study type (Anatomic Area of interest) / Radiographic Views* supported for dislocation detection: -Shoulder / AP, Lateral, Axillary -Elbow / AP, Lateral -Hand / PA, Lateral, Oblique Study type (Anatomic Area of interest) / Radiographic Views* supported for joint effusion detection: -Elbow / AP, Lateral -Forearm / AP, Lateral -Arm / AP, Lateral *Definitions of anatomic area of interest and radiographic views are consistent with the ACR-SPR-SSR Practice Parameter for the Performance of Radiography of the Extremities guideline.
Device Story
Rayvolve is a standalone CAD software using supervised deep learning to detect/localize fractures, dislocations, and joint effusions on musculoskeletal X-rays. It integrates with hospital DICOM node servers/PACS; it filters/downloads CR and DX modality images, processes them, and outputs annotated images with bounding zones for abnormalities or text indicating no abnormality. It is used in clinical settings by radiologists and emergency physicians. The device does not operate autonomously; it provides AI-aided analysis as an adjunct to clinical review. Users review the original image alongside the AI-processed output to confirm findings. By highlighting potential abnormalities, it aims to improve diagnostic accuracy and efficiency, reducing reader analysis time. It is not intended to replace physician diagnosis.
Clinical Evidence
No clinical studies conducted. Bench testing included a standalone performance study on 6,246 radiographs (AUC 97.47%, sensitivity 96.88%, specificity 87.29%). Two MRMC studies (n=178 and n=225) evaluated dislocation detection performance among 20 readers (10 specialists, 10 non-specialists). Results showed improved reader performance with AI: AUC increased from 0.8989 to 0.9451; sensitivity improved from 0.8489 to 0.9146; specificity improved from 0.8826 to 0.9343; and mean analysis time per image decreased by 14% (3.8 seconds).
Technological Characteristics
Standalone software; supervised deep learning algorithm. Operates on cloud platform with local network connectivity to DICOM node servers. Compatible with CR and DX image modalities. No direct patient contact; no electromagnetic, magnetic, or biocompatibility testing required. DICOM standard compliant.
Indications for Use
Indicated for adult and pediatric patients (≥2 years) to assist radiologists and emergency physicians in detecting fractures, dislocations, and joint effusions on musculoskeletal X-rays (specific views for ankle, clavicle, elbow, forearm, hip, humerus, knee, pelvis, shoulder, tibia/fibula, wrist, hand, foot, ribs, and spine).
Regulatory Classification
Identification
A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.
Special Controls
A radiological computer assisted detection and diagnosis software must comply with the following special controls: Design verification and validation must include: 1. i. A detailed description of the image analysis algorithm, including but not limited to a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable. iii. Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contain a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment. iv. Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of but not limited to clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in 1(iii) above. v. Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures. 2. Labeling must include the following: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output. iii. A detailed description of the intended user, and any user training materials as programs that addresses appropriate reading protocols for the device to ensure that the end user is fully aware of how to interpret and apply the device output. iv. A detailed description of the device inputs and outputs. v. A detailed description of compatible imaging hardware and imaging protocols. vi. Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality or for certain subpopulations), as applicable. vii. A detailed summary of the performance testing, including: test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
*e.g.,* receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
*e.g.,* receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
*e.g.,* poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
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FDA U.S. FOOD & DRUG ADMINISTRATION
May 12, 2026
AZmed
Shanni Zeitoun
QARA Team
10 Rue D'Uzès
Paris,
France
Re: K260378
Trade/Device Name: Rayvolve
Regulation Number: 21 CFR 892.2090
Regulation Name: Radiological Computer-Assisted Detection And Diagnosis Software
Regulatory Class: Class II
Product Code: QBS
Dated: April 1, 2026
Received: April 1, 2026
Dear Shanni Zeitoun:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13484 clause 8.3 (Nonconforming product), and ISO 13485 clause 8.5 (Corrective and preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See
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K260378 - Shanni Zeitoun
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the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Jessica Lamb, PhD
Assistant Director
Imaging Software Team
DHT8B: Division of Radiological Imaging Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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# Indications for Use
| Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K260378 | ? |
| --- | --- | --- |
| Please provide the device trade name(s). | | ? |
| Rayvolve | | |
| Please provide your Indications for Use below. | | ? |
| Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures, joint effusions and dislocations during the review of radiographs of the musculoskeletal system. Rayvolve is indicated for the adult and pediatric population (≥2 years). Rayvolve is indicated for radiographs of the following industry-standard radiographic views and study types. Rayvolve is indicated for use in adult and pediatric populations to help detect fracture, dislocations and joint effusions on osteoarticular X-rays of the following anatomic area of interest/radiographic views: | | |
| Study type (Anatomic Area of interest) / Radiographic Views* supported for fracture detection: - Ankle / AP, Lateral, Oblique - Clavicle / AP, AP Angulated View - Elbow / AP, Lateral - Forearm / AP, Lateral - Hip / AP, Frog leg lateral - Humerus / AP, Lateral - Knee / AP, Lateral - Pelvis / AP - Shoulder / AP, Lateral, Axillary - Tibia/fibula / AP, Lateral - Wrist / PA, Lateral, Oblique -Hand / PA, Lateral, Oblique -Foot / AP, Lateral, Oblique. -Ribs AP -Spine PA, Lateral | | |
| Study type (Anatomic Area of interest) / Radiographic Views* supported for dislocation detection: -Shoulder / AP, Lateral, Axillary -Elbow / AP, Lateral -Hand / PA, Lateral, Oblique | | |
| Study type (Anatomic Area of interest) / Radiographic Views* supported for joint effusion detection: -Elbow / AP, Lateral -Forearm / AP, Lateral -Arm / AP, Lateral | | |
| *Definitions of anatomic area of interest and radiographic views are consistent with the ACR-SPR-SSR Practice Parameter for the Performance of Radiography of the Extremities guideline. | | |
| Please select the types of uses (select one or both, as applicable). | ☑ Prescription Use (21 CFR 801 Subpart D) ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | |
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Please select the age group(s) for which the device(s) is to be used.
☐ Neonates/Newborns (Birth to < 29 days old)
☐ Infants (29 days old to < 2 years old)
☑ Children (2 years old to < 12 years old)
☑ Adolescents (12 years old to < 22 years old)
☑ Adults (22 years old and greater)
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azmed
K260378
# RAYVOLVE
510K Summary
1
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azmed
# 1. Submitter
Submitted date: 2026-02-04
| Submitter | AZmed SAS 6 rue Léonard de Vinci 53000 Laval, France Phone: +33 6 43 31 51 38 |
| --- | --- |
| Contact person | Shanni ZEITOUN QARA team 6 rue Léonard de Vinci 53000 Laval, France Phone: +33 6 67 36 64 93 Mail: shanni@azmed.co |
# 2. Device identification
Table 1: Submitter information
| Name of the Device | Common or Usual Name | Regulatory section | Classification | Product Code | Panel |
| --- | --- | --- | --- | --- | --- |
| Rayvolve | Rayvolve | 21 CFR 892.2090 | Class II | QBS | 90 (Radiology) |
# 3. Predicate device
The legally marketed device for which AZmed is claiming equivalence is identified as follows:
Table 2: Device identification
| Manufacturer | Product Name | 510K Number |
| --- | --- | --- |
| AZmed | Rayvolve | K240845 |
Table 3: Predicate device
# 4. Device description
The medical device is called Rayvolve. It is a standalone software that uses machine learning techniques to detect and localize fractures, dislocations, and joint effusions on osteoarticular X-rays. Rayvolve is intended to be used as an aided diagnosis device and does not operate autonomously. It is intended to work in combination with DICOM node servers. When remotely connected to a medical center DICOM Node server, Rayvolve directly interacts with the DICOM files to output the prediction (potential presence or absence of fracture, dislocation, or joint effusion).
Rayvolve has been developed to use the current edition of the DICOM image standard. DICOM is the international standard for transmitting, storing, retrieving, printing, processing, and displaying medical imaging.
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# azmed
Using the DICOM standard allows Rayvolve to interact with existing Picture Archive and Communication Systems (e.g., PACS), and clinical-grade image viewers. The device is designed for running on a cloud platform and connected to the radiology center's local network, and can interact with the DICOM Node server.
Rayvolve filters and downloads only X-rays with modalities (CR and DX) and organs determined from the DICOM Node server.
Abnormalities are directly identified on the X-ray by bounding zones, and if there is no abnormality, a sentence at the top of the X-ray indicates it. After analysis, the device sends edited X-rays to the DICOM Node server.
Note: The initial image in the DICOM Node server is by no means deleted or modified.
When the radiologist downloads the images to be analyzed from the DICOM Node server to his workstation (DICOM Node server-client), the initial image appears first, followed by the image processed by Rayvolve.
The analyzed images help radiologists and physicians to diagnose abnormalities.
Note: Rayvolve does not intend to replace medical doctors. The instructions for use are strictly and systematically transmitted to each user and used to train them on Rayvolve's use.
## 5. Intended use and indication for use
Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures, joint effusions and dislocations during the review of radiographs of the musculoskeletal system.
Rayvolve is indicated for the adult and pediatric population (≥2 years).
Rayvolve is indicated for radiographs of the following industry-standard radiographic views and study types.
Rayvolve is indicated for use in adult and pediatric populations to help detect fracture, dislocations and joint effusions on osteoarticular X-rays of the following anatomic area of interest/radiographic views:
Study type (Anatomic Area of interest) / Radiographic Views* supported for fracture detection:
- Ankle / AP, Lateral, Oblique
- Clavicle / AP, AP Angulated View
- Elbow / AP, Lateral
- Forearm / AP, Lateral
- Hip / AP, Frog leg lateral
- Humerus / AP, Lateral
- Knee / AP, Lateral
- Pelvis / AP
- Shoulder / AP, Lateral, Axillary
- Tibia/fibula / AP, Lateral
- Wrist / PA, Lateral, Oblique
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# azmed
- Hand / PA, Lateral, Oblique
- Foot / AP, Lateral, Oblique.
- Ribs AP
- Spine PA, Lateral
Study type (Anatomic Area of interest) / Radiographic Views* supported for dislocation detection:
- Shoulder / AP, Lateral, Axillary
- Elbow / AP, Lateral
- Hand / PA, Lateral, Oblique
Study type (Anatomic Area of interest) / Radiographic Views* supported for joint effusion detection:
- Elbow / AP, Lateral
- Forearm / AP, Lateral
- Arm / AP, Lateral
* Definitions of anatomic area of interest and radiographic views are consistent with the ACR-SPR-SSR Practice Parameter for the Performance of Radiography of the Extremities guideline.
## 6. Substantial Equivalence Discussion
The comparison chart below provides evidence to facilitate the substantial equivalence determination between Rayvolve to the predicate device (K240845) concerning the intended use, technological characteristics, and principle of operation versus the cited predicate device.
| Comparison to predicate device | Rayvolve - Predicate (K240845) | Rayvolve - Subject device 510(k) file (K260378) | Comparison to the predicate |
| --- | --- | --- | --- |
| Device Name | Rayvolve | Rayvolve | Same |
| Manufacturer | AZmed SAS | AZmed SAS | Same |
| 510 (k) # | K240845 | K260378 | N/A |
| Regulation Number | 21 CFR 892.2090 | 21 CFR 892.2090 | Same |
| Class | II | II | Same |
| Product Code | QBS | QBS | Same |
| Device Panel | Radiology | Radiology | Same |
| Level of Concern | Moderate | Moderate | Same |
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czmed
| Comparison to predicate device | Rayvolve - Predicate (K240845 ) | Rayvolve - Subject device 510(k) file (K260378) | Comparison to the predicate |
| --- | --- | --- | --- |
| Intended use | Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculoskeletal system. | Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculoskeletal system. | Same |
| Intended user | Radiologists and emergency physicians | Radiologists and emergency physicians | Same |
| Intended patient population | Adult and pediatric population | Adult and pediatric population | Same |
| Image modality | X-Ray | X-Ray | Same |
| Anatomic Areas of Interest | Ankle Clavicle Elbow Forearm Hip Humerus Knee Pelvis Shoulder Tibia/fibula Wrist Hand Foot | Ankle Clavicle Elbow Forearm Hip Humerus Knee Pelvis Shoulder Tibia/fibula Wrist Hand Foot Spine Ribs | Expanded anatomy; does not alter intended use |
| Clinical findings | Fractures | Fractures, dislocations and joint effusions | Expanded pathology scope; addressed via clinical data. |
| Machine learning technology | Supervised Deep learning | Supervised Deep learning | Same |
| Image source | DICOM node (e.g, imaging device, | DICOM node (e.g, imaging device, intermediate, | Same |
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czmed
| Comparison to predicate device | Rayvolve - Predicate (K240845) | Rayvolve - Subject device 510(k) file (K260378) | Comparison to the predicate |
| --- | --- | --- | --- |
| | intermediate, DICOM node, PACS system, etc) | DICOM node, PACS system, etc) | |
| Image viewing | PACS system Image annotations toggled on or of | PACS system Image annotations toggled on or of | **Same** |
| Privacy | HIPAA Compliant | HIPAA Compliant | **Same** |
| Platform | On-premise, on cloud, secure local processing and delivery of DICOM images (eg:PACS) | On-premise, on cloud, secure local processing and delivery of DICOM images (eg:PACS) | **Same** |
| Warnings | Rayvolve is an adjunct tool and does not replace the role of the users: they must not use the output of Rayvolve as primary interpretation. The users may use Rayvolve only for the intended anatomical regions indicated in this operating manual. The users may rely on their own diagnostic if ever Rayvolve detects fractures from anatomical areas outside the indication for use. The same bounding box can contain several adjacent fractures: the users shall proceed to a concurrent read with the original X-ray. It is possible that a bounding box hides a part of an adjacent fracture; the users shall proceed to a concurrent read with the original X-ray. | The images provided by Rayvolve are AI-aided automated analysis only. It shall not replace a physician's diagnosis. A disclaimer is displayed on the duplicate and the report generated by Rayvolve. The users may use Rayvolve only for the intended anatomical regions indicated in this operating manual. Rayvolve is not intended to detect abnormalities from anatomical areas of interest outside the indication for use, the users should review original images for all other suspected pathologies and other anatomical regions and shall rely on their own diagnostic if ever Rayvolve detects abnormalities from anatomical areas outside the indication for use. The same bounding zone | **Same, only wording improvement.** |
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czmed
| Comparison to predicate device | Rayvolve - Predicate (K240845 ) | Rayvolve - Subject device 510(k) file (K260378) | Comparison to the predicate |
| --- | --- | --- | --- |
| | Rayvolve is not intended to detect fractures in other modalities that X-rays Rayvolve is not intended to detect fractures from anatomical areas of interest outside the indication for use: user should review original images for all other suspected pathologies and other anatomical regions Poor image quality can affect Rayvolve’s detection of fracture: the users shall proceed to a concurrent read with the original X-ray. In the event of a significant change in Rayvolve’s performance, please contact AZmed. Rayvolve is a diagnostic aid, only the users’ diagnosis remain valid for the patient. Any serious incident occurring in connection with the device should be notified to AZmed and to the FDA. | can contain several adjacent pathologies: the users shall proceed to a concurrent read with the original X-ray. It is possible that a bounding zone hides a part of an adjacent pathology. The users shall proceed to a concurrent read with the original X-ray Rayvolve is not intended to detect pathologies in other modalities than X-rays. Poor image quality can affect Rayvolve’s detection of pathologies: the users shall proceed to a concurrent read with the original X-ray In the event of a significant change in Rayvolve’s performance, please contact AZmed. Rayvolve is a diagnostic aid, only the users’ diagnosis remains valid for the patient. Any serious incident occurring in connection with the device should be notified to AZmed and to the competent authority. | |
| Electromagnetic compatibility and electrical safety | N/A, Rayvolve is a standalone software and is not subject to electromagnetic testing. Therefore no electromagnetic | N/A, Rayvolve is a standalone software and is not subject to electromagnetic testing. Therefore no electromagnetic | Same |
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| Comparison to predicate device | Rayvolve - Predicate (K240845) | Rayvolve - Subject device 510(k) file (K260378) | Comparison to the predicate |
| --- | --- | --- | --- |
| | compatibility and electrical safety is required. | compatibility and electrical safety is required. | |
| Magnetic resonance | N/A, Rayvolve is a standalone software and is not subject to magnetic resonance. Therefore no magnetic testing is required. | N/A, Rayvolve is a standalone software and is not subject to magnetic resonance. Therefore no magnetic testing is required. | Same |
| Animal and/or Cadever Testing | N/A, Rayvolve is a standalone software | N/A, Rayvolve is a standalone software | Same |
| Biocompatibility | N/A, Rayvolve is a standalone software with no direct or indirect patient or user contacting components. Therefore no biocompatibility is required. | N/A, Rayvolve is a standalone software with no direct or indirect patient or user contacting components. Therefore no biocompatibility is required. | Same |
| Output | Abnormality localization (fracture) | Abnormality localization (fracture, dislocation (shoulder, elbow, hand), and effusion (elbow/forearm/arm)) | Same Output structure. |
| Risk Profile | Risk of FN/FP on MSK fractures | Risk of FN/FP on MSK abnormalities (same risk types) | Same risk nature |
Table 4: Comparison between the predicate and subject devices
AZmed claims the substantial equivalence of Rayvolve with the predicate Rayvolve (K240845) based on the same functional principle of the software algorithms, the same technological characteristics, and the intended purpose of the software algorithm. Any differences between the devices do not raise new questions of safety or effectiveness.
## 7. Performance data
### a. Software verification and validation testing
Software development, verification, and validation activities for the device were conducted in accordance with applicable FDA guidance. The software was verified
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# azmed
and validated against the defined software requirements to confirm that the device performs as intended.
A device hazard analysis was performed, and appropriate risk control measures were implemented to mitigate identified risks. The results of software testing demonstrate that all software requirements were met and that the device functions as intended. These results support the substantial equivalence of the Rayvolve device to the predicate device.
Validation activities included a usability study of Rayvolve under normal conditions for use. The study demonstrated:
- Non-invasive usability because users' habits are unchanged,
- Comprehension of the instructions for use provided with the device.
## b. Bench testing
### i. Standalone performance study
To include spine and ribs fractures, and dislocations and joint effusions, AZmed conducted a standalone performance assessment on 6246 radiographs to detect:
- Fractures (cervical spine, thoracic spine, lumbar spine, ribs)
- Dislocations (shoulder, elbow, hand (finger))
- Effusions (elbow, forearm, arm)
for all views in the indication for use.
Within this standalone performance study, all the sensitivity, specificity, and AUC metrics have been computed per radiograph.
The results of standalone testing demonstrated that Rayvolve detects MSK abnormalities on MSK X-rays with high sensitivity (96.88%, 95% Wilson's Confidence Interval (CI): 96.10%; 97.51), high specificity (87.29%; 95% Wilson's CI: 86.21%; 88.30%) and high Area Under The Curve (AUC) of the Receiver Operating Characteristic (ROC) (97.47%; 95% Bootstrap CI: 97.05%; 97.88%) at the case-level.
| ANATOMICAL AREA | AUC (Bootstrapped CI) | Sensitivity (95% Wilson's CI) | Specificity (95% Wilson's CI) |
| --- | --- | --- | --- |
| C-Spine | 0.9581 (0.9406; 0.9736) | TP=218 FP=51 TN=304 FN=8 | |
| | | 0.9646 (0.9317; 0.9820) | 0.8563 (0.8160; 0.8890) |
| T-Spine | 0.9604 (0.9442; 0.9743) | TP=270 FP=62 TN=398 FN=11 | |
| | | 0.9609 (0.9313; 0.9780) | 0.8652 (0.8310; 0.8934) |
| L-Spine | 0.9662 (0.9527; 0.9774) | TP=223 FP=62 TN=354 FN=8 | |
| | | 0.9654 | 0.8510 |
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| | | (0.9332; 0.9823) | (0.8135; 0.8820) |
| --- | --- | --- | --- |
| Rib | 0.9743 (0.9640; 0.9841) | TP=316 FP=56 TN=452 FN=7 | |
| | | 0.9783 (0.9559; 0.9895) | 0.8898 (0.8595; 0.9141) |
| Shoulder Dislocation | 0.9687 (0.9520; 0.9828) | TP=222 FP=54 TN=327 FN=7 | |
| | | 0.9694 (0.9383; 0.9851) | 0.8583 (0.8197; 0.8897) |
| Elbow Dislocation | 0.9720 (0.9565; 0.9862) | TP=189 FP=46 TN=271 FN=7 | |
| | | 0.9643 (0.9281; 0.9826) | 0.8549 (0.8119; 0.8894) |
| Hand Dislocation (Finger) | 0.9798 (0.9657; 0.9909) | TP=173 FP=37 TN=269 FN=5 | |
| | | 0.9719 (0.9359; 0.9879) | 0.8791 (0.8378; 0.9110) |
| Elbow Joint Effusion | 0.9785 (0.9674; 0.9885) | TP=277 FP=59 TN=467 FN=7 | |
| | | 0.9754 (0.9500; 0.9880) | 0.8878 (0.8580; 0.9120) |
| Forearm Joint Effusion | 0.9652 (0.9457; 0.9817) | TP=163 FP=26 TN=207 FN=6 | |
| | | 0.9645 (0.9247; 0.9836) | 0.8884 (0.8415; 0.9227) |
| Arm Joint Effusion | 0.9666 (0.9514; 0.9798) | TP=219 FP=43 TN=358 FN=7 | |
| | | 0.9690 (0.9375; 0.9849) | 0.8928 (0.8587; 0.9194) |
Table 5 - Rayvolve performance per anatomic areas on all radiographs.
The results of the study demonstrated that Rayvolve:
- detects fractures, dislocations and effusions in radiographs with similar performances on the adult population and on the pediatric population (≥2years),
- performs with high accuracy across study types (anatomic areas of interest, views, patient age, patient position, sex and machine) and across potential confounders such as different X-ray manufacturers.
## ii. MRMC
AZmed conducted two multi-reader, multi-case (MRMC) retrospective studies to determine the impact of Rayvolve on reader performance in diagnosing dislocations.
A first MRMC study investigated 178 MSK radiographs across various confounders.
A second MRMC was carried out on 225 MSK radiographs to evaluate the impact of Rayvolve on readers' performance per view of acquisition.
Twenty readers, including ten specialists (MSK radiologists) and ten non-specialists
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# azmed
(from other specialties), identified dislocations without AI and then with AI assistance in two sessions, separated by a one-month washout period.
Within this MRMC study, all the sensitivity, specificity, and AUC metrics have been computed per image.
The study demonstrated an improvement in the performance of readers per image when aided by Rayvolve as measured by the AUC. The AUC in Rayvolve aided reads (0.9451 (95% CI: 0.9380; 0.9523)) is higher than in Rayvolve unaided reads (0.8989 (95% CI: 0.8892; 0.9087)).
In particular, at an image level, the results demonstrated that:
- Reader sensitivity per image was significantly improved from 0.8489 (95% Wilson's CI: 0.8315; 0.8648) to 0.9146 (95% Wilson's CI: 0.9007; 0.9267)
- Reader specificity per image was improved from 0.8826 (95% Wilson's CI: 0.8668; 0.8967) to 0.9343 (95% Wilson's CI: 0.9218; 0.9449)
- Reader analysis time per image was improved from 27.25 seconds to 23.42 seconds, a difference of 3.8 seconds, thus the analysis time has been reduced by 14%.
## First MRMC
## Second MRMC
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MRMC Dislocations (View Only)
Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (ROC) of the second MRMC (image-level).
iii. Clinical data
No clinical studies were conducted in support of the 510(k) submission of Rayvolve.
8. Conclusion
Both the proposed device (Rayvolve) and the predicate device (K240845) are computer-assisted detection and diagnostic devices that accept as input radiographs in DICOM format and use machine learning techniques to identify and highlight pathologies in the adult and pediatric population (≥2 years).
The overall design and development of the software show that the device performs as intended and the differences in indications for use including the addition of spine, and ribs fractures, dislocations and joint effusion detection do not raise different questions of safety and effectiveness.
The results of standalone and the MRMCs demonstrate that Rayvolve performs according to the specifications and meets user needs and intended use.
Therefore, the Rayvolve subject device and the Rayvolve predicate device (K240845) are substantially equivalent.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
