(181 days)
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
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.
Here's a breakdown of the acceptance criteria and study details for the DeepTek CXR Analyzer v1.0, based on the provided FDA 510(k) submission document:
Acceptance Criteria and Reported Device Performance
The core acceptance criteria are based on the performance metrics of the standalone assessment and the clinical performance assessment. The document states that the device's performance was evaluated by measuring sensitivity, specificity, AUROC (Area Under the Receiver Operating Characteristic curve) for detection, and wAFROC-FOM (weighted Alternative Free-Response Receiver Operating Characteristic Figure of Merit) for localization. For the clinical study, the primary objective was to demonstrate that the wAFROC-FOM for aided readings was superior to unaided readings.
Table 1: Acceptance Criteria (Implied) and Reported Device Performance (Standalone)
| Metric (Image-Level Detection) | Target (Implied Acceptance) | Reported Performance [95% CI] |
|---|---|---|
| Sensitivity | (High) | |
| Lungs | 0.903 [0.887-0.914] | |
| Pleura | 0.924 [0.902-0.932] | |
| Cardiac | 0.924 [0.890-0.952] | |
| Hardware | 0.947 [0.936-0.955] | |
| Aggregate | 0.926 [0.917-0.933] | |
| Specificity | (High) | |
| Lungs | 0.937 [0.927-0.948] | |
| Pleura | 0.897 [0.879-0.911] | |
| Cardiac | 0.930 [0.925-0.941] | |
| Hardware | 0.947 [0.939-0.954] | |
| Aggregate | 0.933 [0.925-0.938] | |
| AUROC | (High) | |
| Lungs | 0.971 [0.968-0.976] | |
| Pleura | 0.964 [0.954-0.970] | |
| Cardiac | 0.978 [0.968-0.985] | |
| Hardware | 0.980 [0.976-0.983] | |
| Aggregate | 0.974 [0.970-0.977] |
Table 2: Acceptance Criteria (Implied) and Reported Device Performance (Standalone Localization)
| Metric (ROI-Level Localization) | Target (Implied Acceptance) | Reported Performance [95% CI] |
|---|---|---|
| wAFROC-FOM | (High) | |
| 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 3: Acceptance Criteria (Clinical Study Null/Alternate Hypothesis) and Reported Device Performance (Clinical Study)
| Metric (Clinical wAFROC-FOM) | Null Hypothesis (H0) | Alternate Hypothesis (H1) | Reported Performance [95% CI] |
|---|---|---|---|
| wAFROC-FOM aided | 0.893 [0.871-0.914] | ||
| wAFROC-FOM unaided | 0.821 [0.791-0.852] | ||
| Difference (Aided - Unaided) | ≤ 0 (No improvement or worse) | > 0 (Superiority of aided) | **0.072 (p |
§ 892.2070 Medical image analyzer.
(a)
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.(b)
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.