9791 data points collected from multiple sites and cases in China.
—
Retrospective study of 295 non-contrast head CT scans from four U.S. states.
3 (US board-certified neuroradiologists)
Indications for Use
uAI Easy Triage ICH is a radiological computer-assisted triage and notification software device indicated for analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected positive findings of Intracranial Hemorrhage (ICH).
Device Story
uAI Easy Triage ICH is a radiological computer-assisted triage and notification software. It processes non-enhanced head CT images to identify characteristics consistent with intracranial hemorrhage (ICH). The device operates in parallel with standard-of-care image interpretation. It receives DICOM files, executes a CNN-based triage algorithm, and provides active/passive notifications to radiologists via a platform interface. Active notifications include an alert icon, case count, and status bar with patient details; passive notifications appear as icons in the patient list. The system includes a DICOM image preview feature for informational purposes only. The device does not alter original images or provide diagnostic markers. By flagging suspected ICH cases, the device assists radiologists in prioritizing their worklist, potentially reducing time to notification and improving clinical workflow efficiency.
Clinical Evidence
Retrospective validation study using 295 non-contrast head CT scans (147 positive for ICH, 148 negative) from four U.S. states. Ground truth established by majority read of 3 U.S. board-certified neuroradiologists. Sensitivity: 92% (95% CI: 86%-96%); Specificity: 95% (95% CI: 90%-98%). Average time to notification: 41.1 seconds (95% CI: 40.1-42.1). Subgroup analysis performed across age, gender, race, and CT equipment manufacturers.
Technological Characteristics
Standalone software; utilizes CNN-based AI algorithm for image analysis. Compatible with DICOM standard (NEMA PS 3.1-3.20). Operates on the uAI Easy Triage platform. Cybersecurity controls per FDA guidance. Software lifecycle per IEC 62304. Risk management per ISO 14971.
Indications for Use
Indicated for analysis of non-enhanced head CT images to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected Intracranial Hemorrhage (ICH).
Regulatory Classification
Identification
Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.
Special Controls
Radiological computer aided triage and notification software must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm 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 effective triage (e.g., improved time to review of prioritized images for pre-specified clinicians). iii. Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results). 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 intended user and user training that addresses appropriate use protocols for the device. iii. Discussion of 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 for certain subpopulations), as applicable. iv. A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images. v. Device operating instructions. vi. A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, 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 notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm 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 effective triage (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
*e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(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 intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of 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 for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (*e.g.,* confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
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September 24, 2024
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, with the letters FDA in a blue box. To the right of the blue box is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Shanghai United Imaging Intelligence Co., Ltd. % Nima Akhlaghi Director, Digital Health, AI & Imaging Center Lead MCRA Digital Health 803 7th Street NW, 3rd Floor WASHINGTON, DISTRICT OF COLUMBIA 20001
Re: K242292
Trade/Device Name: uAI Easy Triage ICH Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological Computer Aided Triage And Notification Software Regulatory Class: Class II Product Code: QAS Dated: August 1, 2024 Received: August 2, 2024
Dear Nima Akhlaghi:
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.
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"
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(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 System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 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 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-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (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-device-advicecomprehensive-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-device-safety/medical-device-reportingmdr-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/medicaldevices/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 the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatory
{2}------------------------------------------------
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,
Samuel for
Jessica Lamb, Ph.D. 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
Submission Number (if known)
K242292
Device Name
uAl Easy Triage ICH
#### Indications for Use (Describe)
uAl Easy Triage ICH is a radiological computer-assisted triage and notification software device indicated for analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected positive findings of Intracranial Hemorrhage (ICH).
Type of Use (Select one or both, as applicable)
> Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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# 510 (k) SUMMARY
# 1. Date of Preparation:
August 01, 2024
### 2. Sponsor Identification
Shanghai United Imaging Intelligence Co., Ltd. No. 701 Yunjin Road, Xuhui District, Shanghai, 200232, PEOPLE'S REPUBLIC OF CHINA
Primary Contact Correspondent : MCRA Digital Health Contact Person: Nima Akhlaghi Position: Director, Digital Health, AI & Imaging Center Lead Tel: 202-742-3889 Email: nakhlaghi(@mcra.com
Applicant Contact Person: Zhao Xiaojing Position: Quality & Regulatory Manager Tel: +86-021-67076888-5386 Email: xiaojing.zhao@uii-ai.com
## 3. Identification of Proposed Device
Trade Name: uAI Easy Triage ICH Common Name: Radiological computer aided triage and notification software Model(s): uAI Easy Triage ICH
### Regulatorv Information
Classification Name: Radiological computer aided triage and notification software Classification: II Product Code: QAS Regulation Number: 21 CFR 892.2080 Review Panel: Radiology
- 4. Identification of Predicate Device(s)
Page 1 of 12
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Predicate Device 510(k) Number: K221716 Device Name: CINA
### 5. Device Description
uAI Easy Triage ICH is a radiological computer-assisted triage and notification software intended to assist radiologists by flagging potential intracranial hemorrhage (ICH) in non-contrast head CT images. The Triage Software is a component of the uAI Easy Triage platform, a comprehensive medical imaging communication system designed to integrate and deploy specialized image processing applications.
The uAI Easy Triage ICH algorithm uses artificial intelligence CNN (convolutional neural networks) and advanced image processing to triage the non-contrast CT images for suspicious intracranial hemorrhage.
To train and internally test this ICH CADt device, a total of 9791 data points were collected from multiple sites as well as cases in China. Only cases that met the inclusion criteria were used. The reference standard (ground truth) was established in the form of ICH positive/negative by radiologists.
To clarify, 9791 cases were only used for training and internal testing and final validation data was from USA and independent from those used for training and internal testing.
### 6. Indications for use
uAI Easy Triage ICH is a radiological computer-assisted triage and notification software device indicated for analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected positive findings of Intracranial Hemorrhage (ICH).
# 7. Summary of Technological Characteristics
uAI Easy Triage ICH is a radiological computer-assisted triage and notification software intended to assist radiologists by flagging potential ICH in non-contrast head CT images. The software is a component of the uAI Easy Triage platform, a
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comprehensive medical imaging communication system designed to integrate and deploy specialized image processing applications.
The uAI Easy Triage ICH alerts users to new studies with suspicious ICH findings via pop-up notifications. The software provides both active and passive notification mechanisms. Active notifications are presented as an alert icon with a count of pending cases, and an alert status bar displaying patient details, suspected findings, and the time of examination. Passive notifications are represented by an icon beside the patient's name in the list for cases with detected ICH findings. Additionally, the application offers a DICOM image preview feature for radiologists to review. This preview is strictly informational, devoid of diagnostic markers, and is not to be used for definitive diagnosis.
The uAI Easy Triage ICH embodies the core algorithmic technology that identifies image characteristics consistent with intracranial hemorrhage. The application reads DICOM files, verifies their compatibility with the prescribed acquisition protocols, executes the triage algorithm, and communicates findings in DICOM format, compatible with the uAI Easy Triage Platform.
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# Table 1 Substantial Equivalence Table
| Item | Proposed Device<br>uAI Easy Triage ICH | Predicate Device<br>CINA (K221716) | Remark |
|---------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Classification Name | Radiological Computer Aided<br>Triage And Notification Software | Radiological Computer Aided<br>Triage And Notification Software | Same |
| Product Code | QAS | QAS | Same |
| Regulation Number | 21 CFR 892.2080 | 21 CFR 892.2080 | Same |
| Device Class | II | II | Same |
| Classification Panel | Radiology | Radiology | Same |
| Intended Use/Indications for<br>Use | uAI Easy Triage ICH is a<br>radiological computer-assisted<br>triage and notification software<br>device indicated for analysis of<br>non-enhanced head CT images.<br>The device is intended to assist<br>hospital networks and trained<br>radiologists in workflow triage by | Cina is a radiological computer<br>aided triage and notification<br>software indicated for use in the<br>analysis of (1) non-enhanced head<br>CT images and (2) CT<br>angiography of the head.<br>The device is intended to assist | Compared to predicate device, the<br>proposed device provides one<br>applications for ICH while the<br>predicate device CINA (K221716)<br>provides two applications for ICH<br>and LVO.<br>uAI Easy Triage ICH and the<br>predicate device CINA(K221716)<br>have the same intended use |
| Item | Proposed Device<br>uAI Easy Triage ICH | Predicate Device<br>CINA (K221716) | Remark |
| | flagging and prioritizing studies<br>with suspected positive findings of<br>Intracranial Hemorrhage (ICH). | hospital networks and trained<br>radiologists in workflow triage by<br>flagging and communicating<br>suspected positive findings of (1)<br>head CT images for Intracranial<br>Hemorrhage (ICH) and (2) head<br>CT angiography for large vessel<br>occlusion (LVO) of the anterior<br>circulation (distal ICA, MCA-M1<br>or proximal MCA-M2).<br>Cina uses an artificial intelligence<br>algorithm to analyze images and<br>highlight cases with detected (1)<br>ICH or (2) LVO on a standalone<br>Web application in parallel to the<br>ongoing standard of care image<br>interpretation. The user is<br>presented with notifications for<br>cases with suspected ICH or LVO | and\indications for use in terms of<br>finding suspected intracranial<br>hemorrhage in non-enhanced head<br>CT, flagging suspected cases, and<br>indicating the case to the attention of<br>the clinician. |
| Item | Proposed Device<br>uAI Easy Triage ICH | Predicate Device<br>CINA (K221716) | Remark |
| | | findings.<br>Notifications include compressed<br>preview images that are meant for<br>informational purposes only, and<br>are not intended for diagnostic use<br>beyond notification. The device<br>does not alter the original medical<br>image, and it is not intended to be<br>used as a diagnostic device.<br>The results of Cina are intended to<br>be used in conjunction with other<br>patient information and based on<br>professional judgement to assist<br>with triage/prioritization of<br>medical images. Notified<br>clinicians are ultimately<br>responsible for reviewing full<br>images per the standard of care. | |
| Item | Proposed Device<br>uAI Easy Triage ICH | Predicate Device<br>CINA (K221716) | Remark |
| User population | Radiologist | Radiologist | Same |
| Anatomical region of interest | Head | Head | Same |
| Data acquisition protocol | Non-contrast CT scan of the head | Non-contrast CT scan of the head<br>or neck and CT angiogram images<br>of the brain | Compared to predicate device, the<br>proposed device supports non-<br>contrast head CT data for ICH<br>application while the predicate device<br>support non-contrast head CT data for<br>ICH application or neck and brain CT<br>angiogram data for LVO application.<br>The difference between the proposed<br>device and the predicate device will<br>not impact the safety and<br>effectiveness of the subject device. |
| View DICOM data | DICOM information about the<br>patient, study and current image | DICOM information about the<br>patient, study and current image | Same |
| Segmentation of region of<br>interest | No; device does not mark,<br>highlight, or direct users' attention<br>to a specific location in the | No; device does not mark,<br>highlight, or direct users' attention<br>to a specific location in the | Same |
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#### UNITED IMAGING C Intelligence
Page 5 of 12
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UNITED IMAGING Intelligence
C
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UNITED IMAGING Intelligence
C
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UNITED IM/GING ██ Intelligence
| 1 |
|---|
| |
| Item | Proposed Device<br>uAI Easy Triage ICH | Predicate Device<br>CINA (K221716) | Remark |
|----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | original image | original image | |
| Algorithm | Artificial intelligence algorithm<br>with database of images | Artificial intelligence algorithm<br>with database of images | Same |
| Notification /<br>Prioritization | Yes | Yes | Same |
| Preview<br>images | Presentation of a preview of the<br>study for initial assessment not<br>meant for diagnostic purposes.<br>The device operates in parallel<br>with the standard of care, which<br>remains the default option for all<br>cases. | Presentation of a preview of the<br>study for initial assessment not<br>meant for diagnostic purposes.<br>The device operates in parallel<br>with the standard of care, which<br>remains the default option for all<br>cases. | Same |
| Alteration of<br>original image | No | No | Same |
| Removal of | No | No | Same |
| Item | Proposed Device<br>uAI Easy Triage ICH | Predicate Device<br>CINA (K221716) | Remark |
| cases from<br>worklist queue | | | |
| Structure | - ICH image processing<br>applications<br>- uAI Easy Triage Platform,<br>including Alert Icon, Patient<br>Management, and Image Viewer. | - LVO and ICH image processing<br>applications<br>- Cina Platform (worklist and<br>Image Viewer) | Compared to predicate device, the<br>proposed device have same software<br>structure and add an alert icon<br>module to pop-up the suspected<br>positive findings of ICH. The<br>difference between the proposed<br>device and the predicate device will<br>not impact the safety and<br>effectiveness of the subject device. |
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UNITED IMΛGING Intelligence 바
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### 8. Performance Data
The following performance data were provided in support of the substantial equivalence determination.
### Biocompatibility
Not Applicable to the proposed device, because the device is stand-alone software.
### Electrical Safety and Electromagnetic Compatibility (EMC)
Not Applicable to the proposed device, because the device is stand-alone software.
### Software Verification and Validation
Software verification and validation testing was provided to demonstrate safety and efficacy of the proposed device.
Per FDA Guidance "GUIDANCE FOR THE CONTENT OF PRE-MARKET SUBMISSIONS FOR SOFTWARE CONTAINED IN DEVICES", dated June 14, 2023, the Level of Concern of the software contained in the proposed device is determined to be: Basic Documentation Level.
Those documentations include:
- · Software Description
- Risk management File
- · Software Requirements Specification (SRS)
- · Software Architecture Diagram
- · Software Development Environment Description
- · Software Verification and Validation
- · Software Version History
- · Cybersecurity Documents
### Animal Study
No animal study was required.
### Clinical Studies
No clinical study was required.
### Performance Testing
Algorithm training of uAI Portal software has been conducted on images collected from China as training dataset. The dataset ensures a variety of data for different gender,
### Page 10 of 12
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age, equipment and CT protocol. Algorithm validation has been conducted on data from the USA. To validate the uAI Easy Triage ICH software from a clinical perspective, the AI-based algorithm contained in the product underwent a retrospective study to evaluate the performance of uAI Easy Triage ICH in terms of sensitivity, specificity and time to notification to identify/notify patients with suspected Intracranial Hemorrhage (ICH) processing non-contrast CT images.
Sensitivity and specificity of uAI Easy Triage ICH in processing of non-contrast head CT have been analyzed by comparison to a ground truth established by majority read of 3 U.S .- board-certified neuroradiologists.
A total of 295 non-contrast CT scans (studies) were obtained from different zip codes across four U.S. states. There were approximately equal numbers of positive and negative cases (147 of images with ICH and 148 without ICH, respectively) included in the analysis.
Regarding the validation of the algorithm, the test data was used independently from that used for training, tuning and internal testing dataset.
The validation data demographic information is summarized below.
| Subgroup | Breakdown | Sample Size |
|--------------------|-----------------------------------|-------------|
| Gender | Male | 49% |
| | Female | 51% |
| Age Ranges (Years) | 18-45 | 17% |
| | 45-65 | 36% |
| | 65+ | 47% |
| Race Group | White | 63% |
| | Black | 18% |
| | others | 19% |
| Equipment | GE | 17% |
| | Philips | 21% |
| | Siemens | 62% |
| ICH Subtypes | Intraparenchymal Hemorrhage (IPH) | 30% |
| | Intraventricular Hemorrhage (IVH) | 17% |
| | Subarachnoid Hemorrhage (SAH) | 24% |
| | Subdural Hemorrhage (SDH) | 22% |
| | Extradural Hemorrhage (EDH) | 0.3% |
Table 2 Clinical testing data subgroup information
Comparing the uAI Easy Triage ICH software output to the ground truth, the sensitivity and specificity of uAI Easy Triage ICH are 92% (95% CI: 86%-96%) and 95% (95% CI: 90%-98%), respectively, exceeding the acceptance criteria of 80%.
Multiple subgroups of interest were considered in the analysis. In fact, the validation data was acquired from different regions across the U.S. to account for race/ethnicity
{15}------------------------------------------------
in the intended U.S. patient population. Detailed subgroup analysis were reported in the labeling.
The uAI Easy Triage ICH Time to Notification was measured during the study. The average Time to Notification in seconds was 41.1 with a 95% confidence interval (CI) for the mean equaling (40.1, 42.1).
In conclusion, the performance of the study for the validation of uAI Easy Triage ICH demonstrates that the device is effective for triage and notification as the performance levels (sensitivity and specificity) meet the predefined acceptance criteria and the time to notification of the device is similar to the predicate device's time.
### Other Standards and Guidance
- · NEMA PS 3.1 3.20 Digital Imaging and Communications in Medicine (DICOM) Set
- · ISO 14971 Medical devices Application of risk management to medical devices (Third Edition 2019-12).
- · IEC 62304 Medical device software Software life cycle processes (Edition 1.1 2015-06 CONSOLIDATED VERSION).
- · Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions (Document issued on September 27, 2023).
# 9. Substantially Equivalent (SE) Conclusion
Compare to the predicate device, the subject device has a subset of the predicate application which resulted in a limited indication for use. The subject device has similar technological characteristics compare to the predicate device and the difference does not raise any new question of safety and effectiveness. Therefore the subject device is substantially equivalent to the proposed predicate.
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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.
