Clinical performance assessment (MRMC study): 300 frontal chest radiographs from 13 U.S. hospitals.
24 (US board-certified radiologists)
Indications for Use
DeepTek CXR Analyzer v1.0 is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies using machine learning techniques to identify, categorize, and highlight suspicious ROIs in one of the following categories: Lungs, Pleura, Cardiac, and Hardware. The device is intended for use as a concurrent reading aid for radiologists. DeepTek CXR Analyzer v1.0 is indicated for adults and transitional adolescents (18 to <22 years old but treated like adults) only.
Device Story
DeepTek CXR Analyzer v1.0 is a CADe software device for radiologists. It receives adult frontal chest radiographs in DICOM format from PACS. Using deep learning algorithms, it analyzes images to detect suspicious ROIs in Lungs, Pleura, Cardiac, and Hardware categories. Output is a secondary capture DICOM image containing the original radiograph with color-coded bounding boxes for detected ROIs or a 'No Suspicious ROI(s) Detected' message. The device is used as a concurrent reading aid; radiologists review annotated images alongside original, unannotated images. It does not provide diagnosis or treatment recommendations. The device is intended to improve reader performance in detecting and localizing suspicious findings, potentially benefiting patients through increased diagnostic accuracy.
Clinical Evidence
Clinical performance was evaluated via a fully-crossed MRMC retrospective study with 24 U.S. board-certified radiologists reading 300 frontal chest radiographs. Primary endpoint was wAFROC-FOM. Aided reading (0.893 [0.871-0.914]) was significantly superior (p<0.0001) to unaided reading (0.821 [0.791-0.852]). 100% of readers showed improvement in wAFROC-FOM when aided. Standalone performance testing on 3,000 scans (NIH and Segmed datasets) showed aggregate sensitivity 0.926, specificity 0.933, and AUROC 0.974.
Indicated for adults and transitional adolescents (18 to <22 years old) to assist radiologists in identifying, categorizing, and highlighting suspicious ROIs (Lungs, Pleura, Cardiac, Hardware) in chest radiographs.
Regulatory Classification
Identification
Medical image analyzers, including computer-assisted/aided detection (CADe) devices for mammography breast cancer, ultrasound breast lesions, radiograph lung nodules, and radiograph dental caries detection, is a prescription device that is intended to identify, mark, highlight, or in any other manner direct the clinicians' attention to portions of a radiology image that may reveal abnormalities during interpretation of patient radiology images by the clinicians. This device incorporates pattern recognition and data analysis capabilities and operates on previously acquired medical images. This device is not intended to replace the review by a qualified radiologist, and is not intended to be used for triage, or to recommend diagnosis.
Special Controls
*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 algorithms including a description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing methods and dataset(s) used to assess whether the device will improve reader performance as intended and to characterize the standalone device performance. Performance testing includes one or more standalone tests, side-by-side comparisons, or a reader study, as applicable.
(iii) Results from performance testing that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
*e.g.,* receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain a sufficient number of cases from important cohorts (*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, 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) 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; and cybersecurity).(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 reading protocol.
(iii) A detailed description of the intended user and user training that addresses appropriate reading protocols for the device.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) 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 or for certain subpopulations), as applicable.(vii) Device operating instructions.
(viii) 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 lesion and organ characteristics, disease stages, and imaging equipment.
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Image /page/0/Picture/0 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, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
October 5, 2023
DeepTek Medical Imaging Pvt Ltd % Carrillo Rory Quality & Regulatory Consultant 3rd Floor, Ideas to Impact, Pallod Farms 3 Behind Vijay Sales, Baner Pune. Maharashtra 411405 INDIA
Re: K231001
Trade/Device Name: DeepTek CXR Analyzer v1.0 Regulation Number: 21 CFR 892.2070 Regulation Name: Medical Image Analyzer Regulatory Class: Class II Product Code: MYN Dated: September 8, 2023 Received: September 8, 2023
Dear Carrillo Rory:
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" (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).
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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 (QS) 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.
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 mediation-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-regulatoryassistance/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,
Lu Jiang
Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems 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
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# Indications for Use
Submission Number (if known)
K231001
Device Name
## DeepTek CXR Analyzer v1.0
### Indications for Use (Describe)
DeepTek CXR Analyzer v1.0 is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies using machine learning techniques to identify, categorize, and highlight suspicious ROIs in one of the following categories: Lungs, Pleura, Cardiac, and Hardware. The device is intended for use as a concurrent reading aid for radiologists. DeepTek CXR Analyzer v1.0 is indicated for adults and transitional adolescents (18 to <22 years old but treated like adults) only.
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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# K231001
# 510(k) Summary
#### 1. General Information
| 510(k) Sponsor | DeepTek Medical Imaging Pvt Ltd |
|-----------------------|------------------------------------------------------------------------------------------------------------|
| Address | 3rd Floor, Ideas to Impact, Pallod Farms 3<br>Behind Vijay Sales, Baner<br>Pune, India, Maharashtra 411405 |
| Correspondence Person | Rory A. Carrillo<br>Regulatory Consultant<br>Cosm |
| Contact Information | Email: rory@cosmhq.com<br>Phone: 415-580-0916 |
| Date Prepared | April 07, 2023 |
#### 2. Proposed Device
| Proprietary Name | DeepTek CXR Analyzer v1.0 |
|---------------------|---------------------------|
| Common Name | CXR Analyzer |
| Classification Name | Medical image analyzer |
| Regulation Number | 892.2070 |
| Regulation Name | Medical image analyzer |
| Product Code | MYN |
| Regulatory Class | II |
#### 3. Predicate Device
| Proprietary Name | Chest-CAD |
|------------------------|------------------------|
| Premarket Notification | K210666 |
| Classification Name | Medical image analyzer |
| Regulation Number | 892.2070 |
| Regulation Name | Medical image analyzer |
| Product Code | MYN |
| Regulatory Class | II |
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Image /page/4/Picture/0 description: The image shows the logo for DeepTek. The logo has the word "DEEPTEK" in blue and orange letters. The letters "DEEP" are in blue, and the letters "TEK" are in orange. To the right of the word are four semi-transparent blue circles that are layered on top of each other.
#### Device Description 4.
DeepTek CXR Analyzer is a computer-assisted detection (CADe) software device developed to assist radiologists in identifying suspicious regions of interest (ROIs) in the following categories: Lungs, Pleura, Cardiac, and Hardware. DeepTek CXR Analyzer detects suspicious ROIs by analyzing adult frontal chest radiographs using deep learning algorithms and provides relevant annotations to assist radiologists with their interpretations.
The device has an authentication graphical user interface, which allows the user to authenticate themselves. The user can connect the PACS with the DeepTek CXR Analyzer using the configuration interface. The user can enter the PACS AE Title, IP address, Listener and Sender Port number to configure the device. Once the device is configured correctly, DeepTek CXR Analyzer receives chest radiographs from the configured PACS in DICOM format as input. DeepTek CXR Analyzer identifies suspicious ROIs in the following categories: Lungs, Pleura, Cardiac, and Hardware, and sends the secondary capture DICOM with AI output to the same PACS over the DICOM protocol. The output DICOM File Processing component creates a DICOM image containing the original radiograph with a message stating that the image was analyzed by DeepTek CXR Analyzer (with information containing manufacturer name, product name, product version, and a link to user manual) and color-coded bounding boxes containing suspected ROIs. If no suspicious ROIs are detected in the image, the output will not contain any bounding boxes and will have a message stating "No Suspicious ROI(s) Detected". In the event of any type of failure in the workflow, a human-readable error message representing the type of failure will be logged in the Logs interface.
DeepTek CXR Analyzer does not make treatment recommendations or provide a diagnosis. Radiologists should review images annotated by DeepTek CXR Analyzer concurrently with original, unannotated images before making the final decision on a case. DeepTek CXR Analyzer is an adjunct tool and does not replace the role of the radiologists. The CAD-generated output should not be used as the primary interpretation by radiologists.
DeepTek CXR Analyzer has been trained using a large and diverse dataset of more than 100,000 chest X-ray images sourced from 30 distinct sites from India, including medical imaging centers, data partners, and medical hospitals, and over 15 different modality manufacturers. The inclusion of such a diverse range of data ensures that the performance of the DeepTek CXR Analyzer generalizes to a wide variety of confounders.
DeepTek CXR Analyzer is not designed to detect conditions other than those classified under the following categories: Lung, Cardiac, Pleura, and Hardware. Radiologists should review original images for all suspected ROIs.
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Image /page/5/Picture/0 description: The image shows the logo for DeepTek. The logo has the word "DEEPTEK" in blue and orange letters. The letters "DEEP" are in blue, and the letters "TEK" are in orange. To the right of the word, there are four semi-transparent blue circles that are stacked on top of each other.
#### .. Indications For Use
The DeepTek CXR Analyzer V1.0 is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies using techniques to identify, categorize, and highlight suspicious ROIs in one of the following categories: Lungs, Pleura, Cardiac, and Hardware. The device is intended for use as a concurrent reading aid for radiologists. DeepTek CXR Analyzer V1.0 is indicated for adults and transitional adolescents (18 to <22 years old but treated like adults) only.
#### Substantial Equivalence 6.
| Feature/<br>Function | Proposed Device<br>DeepTek CXR Analyzer | Predicate Device<br>Chest-CAD (K210666) |
|------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Indications for<br>Use | The DeepTek CXR Analyzer V1.0 is<br>a computer-assisted detection<br>(CADe) software device that<br>analyzes chest radiograph studies<br>using machine learning techniques<br>to identify, categorize, and highlight<br>suspicious ROIs in one of the<br>following categories: Lungs, Pleura,<br>Cardiac, and Hardware. The device<br>is intended for use as a concurrent<br>reading aid for radiologists.<br>DeepTek CXR Analyzer V1.0 is<br>indicated for adults and transitional<br>adolescents (18 to <22 years old but<br>treated like adults) only. | Chest-CAD is a computer-assisted<br>detection (CADe) software device that<br>analyzes chest radiography studies<br>using machine learning techniques to<br>identify, categorize, and highlight<br>suspicious regions of interest (ROI).<br>Any suspicious ROI identified by<br>Chest-CAD is assigned to one of the<br>following categories: Cardiac,<br>Mediastinum/Hila, Lungs,<br>Pleura, Bones, Soft Tissues,<br>Hardware, or Other. The device is<br>intended for use as a concurrent<br>reading aid for physicians.<br>Chest-CAD is indicated for adults<br>only. |
| Image<br>Modality | X-ray | X-ray |
| Study Type | Chest | Chest |
| Image Type | DICOM | DICOM |
| Clinical<br>Output | Identify and mark regions of<br>interest (ROIs) on chest radiographs | Identify and mark regions of interest<br>(ROIs) on chest radiographs |
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Image /page/6/Picture/0 description: The image shows the logo for DeepTek. The word "DEEPTEK" is written in blue and orange letters. The letters "DEEP" are in blue, and the letters "TEK" are in orange. To the right of the word, there are four semi-transparent blue circles that are overlapping each other.
| Feature/<br>Function | Proposed Device<br>DeepTek CXR Analyzer | Predicate Device<br>Chest-CAD (K210666) |
|---------------------------|------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Clinical<br>Finding | Identified ROIs are assigned to one<br>of the following categories: Lungs,<br>Pleura, Cardiac, or Hardware | Identified ROIs are assigned to one of<br>the following categories: Cardiac,<br>Mediastinum/Hila, Lungs, Pleura,<br>Bones, Soft Tissues, Hardware, or<br>Other |
| Intended Users | Radiologists | Physicians |
| Intended User<br>Workflow | Device intended for use as a reading<br>aid for radiologists<br>interpreting chest radiographs | Device intended for use as a reading<br>aid for physicians interpreting chest<br>radiographs |
| Patient<br>Population | Adults with Chest Radiographs | Adults with Chest Radiographs |
| Algorithm<br>Methodology | Artificial Neural Networks | Artificial Neural Networks |
| Platform | Secure on-premise processing and<br>delivery of chest radiographs | Secure cloud-based processing and<br>delivery of chest radiographs |
| Image Source | Digital X-ray | Digital X-ray |
| Image Viewing | Image displayed on PACS system | Image displayed on PACS system |
#### 7. Performance Data
Safety and performance of the DeepTek CXR Analyzer has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification, validation, standalone and clinical performance testing. Additionally, the software validation activities were performed in accordance with IEC 62304 Edition 1.1 2015-06 Medical device software - Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions"
Performance data from standalone and clinical performance assessments are summarized below.
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Image /page/7/Picture/0 description: The image shows the logo for DeepTek. The logo has the word "DEEPTEK" in blue and orange letters. The letters "DEEP" are in blue, and the letters "TEK" are in orange. To the right of the word is a series of blue arcs that are arranged in a circular pattern.
## Standalone Performance Assessment
DeepTek conducted a standalone performance assessment to evaluate the performance of DeepTek CXR Analyzer software device in detection (by measuring sensitivity, specificity, and AUROC) and localization (by measuring wAFROC-FOM) of suspicious ROIs from chest X-rays and classifying each ROI into one of the following four categories: Lungs, Pleura, Cardiac, or Hardware.
The primary objective of this study was to measure and report the performance of DeepTek CXR Analyzer independently in the absence of any interaction with human readers.
The test dataset included 2,000 scans from the NIH Chest X-ray Database and 1,000 scans from the Segmed Insight Platform. The dataset from NIH Chest X-ray Database included chest radiographs from 55.6% male and 44.4% female patients. 30.1% of radiographs included in the study were acquired in AP view and 69.9% of radiographs were acquired in PA view. The dataset from the Segmed Insight Platform included data from 13 different sites across various regions of the United States. The dataset from Segmed Insight Platform included chest radiographs from 45.7% male and 54.3% female patients. 21.6% of the chest radiographs were from the age group 18-35, 38.4% of the chest radiographs were from the age group 36-60, and 40% of the chest radiographs were from patients aged 61 years and above. 40.5% of radiographs included in the study were acquired in AP view and 59.5% of radiographs were acquired in PA view. Out of 1,000 scans, racial information was available for 699 scans with individuals of White (46%), Black (15.7%), Asian (5.8%), and other racial (2.3%) backgrounds. Ethnicity information was available for 286 (28.6%) scans with individuals of Hispanic or Latino (20.2%) and non-Hispanic or non-Latino (8.4%) ethnicity. The data was acquired from the following X-ray imaging devices: Samsung Electronics (25.6%), Fujifilm Corporation (20.8%), Konica Minolta (13.7%), Carestream Health (20.3%), Canon Inc. (7.8%), GE Healthcare (7.7%), PACSGear (3.3%). Siemens (0.6%), and Philips Medical Systems (0.2%). This dataset was not used for the DeepTek CXR Analyzer model training and development. Individuals involved in the model development exercise were not involved in the study design or analysis.
The ground truth for each case and category was established by a panel of 3 U.S. board-certified radiologists with 9, 11, and 25 years of experience, respectively. The ground truth (GT) label for the presence or absence of ROI for each category was defined as the majority opinion of 2 out of the 3 the radiologists. If the majority opinion stated suspicious ROI was present for a particular category, the union of the area encompassed by the bounding boxes made by all annotators identifying suspicious ROI for that particular category was taken as ground truth ROI. If the majority opinion stated suspicious ROI was absent, ROI was not demarcated on the image.
The detection performance and localization performance results were as follows:
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Image /page/8/Picture/0 description: The image shows the logo for DeepTek. The word "DEEPTEK" is written in blue, except for the "TE" which is orange. To the right of the word is a series of four blue circles that are increasingly smaller.
# Detection Performance
The device performance in detecting suspicious ROIs was measured by the area under the receiver operating characteristic (AUROC) curve and the sensitivity and specificity obtained by the device at the recommended operating threshold. AUROCs were highest for Cardiac (0.978 [0.968-0.985]) and Hardware (0.980 [0.976-0.983]) and lowest for Pleura (0.964 [0.954-0.970]). Sensitivity was highest for Hardware (0.947 [0.936-0.955]) and lowest for Lungs 0.903 [0.887-0.914]. Specificity was highest for Hardware (0.947 [0.936-0.955]) and lowest for Pleura (0.897 [0.879-0.911]).
The DeepTek CXR Analyzer demonstrated the following results for detection of ROIs.
| Image-Level Detection Performance | | | |
|-----------------------------------|----------------------|----------------------|---------------------|
| | Sensitivity [95% CI] | Specificity [95% CI] | AUROC [95% CI] |
| Lungs | 0.903 [0.887-0.914] | 0.937 [0.927-0.948] | 0.971 [0.968-0.976] |
| Pleura | 0.924 [0.902-0.932] | 0.897 [0.879-0.911] | 0.964 [0.954-0.970] |
| Cardiac | 0.924 [0.890-0.952] | 0.930 [0.925-0.941] | 0.978 [0.968-0.985] |
| Hardware | 0.947 [0.936-0.955] | 0.947 [0.939-0.954] | 0.980 [0.976-0.983] |
| Aggregate | 0.926 [0.917-0.933] | 0.933 [0.925-0.938] | 0.974 [0.970-0.977] |
Table 1. Sensitivity, specificity, and AUROC for detection of ROIs in Lungs, Pleura, Cardiac, and Hardware by DeepTek CXR Analyzer.
# Localization Performance
The device performance in localizing the suspicious ROIs was measured by the area under the weighted alternative free-response receiver operating characteristic curve as the figure of merit (wAFROC-FOM). DeepTek CXR Analyzer was excellent across all four categories with the highest wAFROC-FOM for Cardiac (0.952 /0.941-0.966)) and Hardware (0.954 /0.948-0.963)), and the lowest for Pleura (0.884 [0.866-0.902]). The device achieved an aggregated wAFROC-FOM of (0.920 [0.908-0.926]) across all chest findings together.
The DeepTek CXR Analyzer demonstrated the following results for localization of ROIs.
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Image /page/9/Picture/0 description: The image shows the logo for DeepTek. The logo has the word "DEEPTEK" in blue and orange letters. The letters "DEEP" are in blue, while the letters "TEK" are in orange. To the right of the word are four semi-transparent blue circles that are overlapping each other.
| ROI-Level Localization Performance | wAFROC-FOM [95% CI] |
|------------------------------------|---------------------|
| Lungs | 0.913 [0.904-0.924] |
| Pleura | 0.884 [0.866-0.902] |
| Cardiac | 0.952 [0.941-0.966] |
| Hardware | 0.954 [0.948-0.963] |
| Aggregate | 0.920 [0.908-0.926] |
Table 2: wAFROC-FOM for localization of ROIs in Lungs, Pleura, Cardiac, and Hardware by DeepTek CXR Analyzer
## Clinical Performance Assessment
DeepTek conducted a fully-crossed multiple reader, multiple case (MRMC) retrospective study to determine the impact of the DeepTek CXR Analyzer on reader performance in detecting and localizing suspicious ROIs in chest radiographs. This study protocol was designed in accordance with FDA's guidance document for industry and FDA staff titled "Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data in Premarket Notification (510(k)) Submissions".
The primary objective of the clinical reader study was to determine whether the performance of readers aided by DeepTek CXR Analyzer is superior to their performance when unaided by DeepTek CXR Analyzer, as determined by the wAFROC-FOM score.
Null hypothesis Ho: wAFROC-FOMaided-WAFROC-FOMumaided ≤ 0
Alternate hypothesis H : wAFROC-FOMaided-WAFROC-FOMunaided > 0
where wAFROC-FOMation-mean wAFROC-FOM for aided reads, and wAFROC FOM maided is the population-mean for unaided reads.
The test dataset consisted of 300 frontal chest radiographs obtained from 13 U.S. hospitals. 24 U.S. board-certified radiologists with varying levels of experience were enrolled in this study. The clinical performance assessment consisted of two independent reading sessions separated by a washout period of 30 days in order to mitigate memory bias. During the first reading session, the readers evaluated each scan in the test dataset while not being aided by the device (unaided reading). During the second reading session, the readers evaluated each scan again, but this time they were aided by the device (aided reading). The evaluation workflow for the unaided and aided readings was identical except that, during the aided reading, a secondary image containing device output markers was shown to the readers in addition to the original image.
DeepTek Medical Imaging Pvt Ltd, 510(k) Submission
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Image /page/10/Picture/0 description: The image shows the word "DEEPTEK" in blue and orange letters. The letters "DEEP" are in blue, while the letters "TEK" are in orange. To the right of the word is a series of four semi-transparent blue circles that are arranged in a row, with each circle being slightly smaller than the one before it. The circles appear to be overlapping each other.
The area under the weighted alternative free-response receiver operating characteristic curve as the figure of merit (wAFROC-FOM) was used for comparing the performance of the readers during the unaided reading sessions with their performance during the aided reading sessions.The wAFROC-FOM for aided readings was 0.893 [0.871-0.914], which was significantly higher (p<0.0001) than the wAFROC-FOM of 0.821 [0.791-0.852] for unaided readings
The DeepTek CXR Analyzer demonstrated the following results in clinical performance assessment.
| Category | wAFROC-FOM | |
|-----------|---------------------|---------------------|
| | Unaided<br>[95% CI] | Aided<br>[95% CI] |
| Lungs | 0.853 [0.820-0.887] | 0.919 [0.896-0.943] |
| Pleura | 0.806 [0.763-0.850] | 0.853 [0.815-0.892] |
| Cardiac | 0.825 [0.781-0.869] | 0.886 [0.842-0.930] |
| Hardware | 0.919 [0.887-0.950] | 0.952 [0.931-0.973] |
| Aggregate | 0.821 [0.791-0.852] | 0.893 [0.871-0.914] |
Table 3: wAFROC-FOM scores for unaided and aided readers when localizing ROIs in Lungs, Pleura, Cardiac, and Hardware
The localization-level clinical performance assessment demonstrated that 24/24 (100%) readers showed an improvement in wAFROC-FOM for localizing suspicious ROIs across all categories when they were aided by DeepTek CXR Analyzer.
| | Summary for Improvement in wAFROC-FOM of readers | | | |
|--|------------------------------------------------------------------------------------------------------------------|--|--|--|
| | 23/24 (96%) readers showed an improvement in wAFROC-FOM for localizing suspicious lungs ROIs | | | |
| | 21/24 (88%) readers showed an improvement in wAFROC-FOM for localizing suspicious pleura ROIs | | | |
| | 24/24 (100%) readers showed an improvement in wAFROC-FOM for localizing suspicious cardiac ROIs | | | |
| | 20/24 (83%) readers showed an improvement in wAFROC-FOM for localizing suspicious hardware ROIs | | | |
| | 24/24 (100%) readers showed an improvement in wAFROC-FOM for localizing suspicious ROIs across<br>all categories | | | |
Table 4: Improvement in wAFROC-FOM for aided readings
{11}------------------------------------------------
Image /page/11/Picture/0 description: The image shows the logo for DeepTek. The logo has the word "DEEPTEK" in blue and orange. The letters "DEEP" are in blue, and the letters "TEK" are in orange. To the right of the word, there are four semi-transparent blue circles that are overlapping each other.
Sensitivity and specificity across all readers and categories during unaided and aided readings are presented in Table 5.
| Reader | Category | Sensitivity | | Specificity | |
|----------------|-----------|---------------------|---------------------|---------------------|---------------------|
| | | Unaided [95% CI] | Aided [95% CI] | Unaided [95% CI] | Aided [95% CI] |
| All<br>readers | Aggregate | 0.810 [0.699-0.902] | 0.886 [0.813-0.945] | 0.911 [0.804-0.972] | 0.949 [0.884-0.990] |
Table 5: Sensitivity and specificity across all readers and categories during the unaided and aided reading sessions
#### 8. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics and performance testing, the DeepTek CXR Analyzer raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety, efficacy, and performance.
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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.