K213566 · Riverain Technologies, Inc. · QFM · Mar 10, 2022 · Radiology
Device Facts
Record ID
K213566
Device Name
ClearRead Xray Pneumothorax
Applicant
Riverain Technologies, Inc.
Product Code
QFM · Radiology
Decision Date
Mar 10, 2022
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2080
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K213566 · Mar 10, 2022
ClearRead Xray Pneumothorax
Riverain Technologies, Inc.
MIMIC-CXR dataset (Beth Israel Deaconess Medical Center); Georgetown University Medical Center clinical image archive
Retrospective clinical images were used as an independent test set to validate the device's performance (AUC, sensitivity, specificity) for pneumothorax triage.
Retrospective clinical data; Independent test set; Chest X-ray; Pneumothorax
Clinical Evidence
Study Design
Population
Comparator
Key Endpoints
Independent Clinical Validation Study; Retrospective cohort study; Follow-up/Duration: Not applicable; Study Period: 2002-2016
Adult (18+) patients undergoing chest X-rays; Sample Size: 1138 cases; Number of Sites: 2 (Beth Israel Deaconess Medical Center and Georgetown University Medical Center)
Not applicable for this study
AUC of the ROC curve, sensitivity, specificity, time-to-notification
Development set: used to update simulation engine. Test set: 600 cases (300 negatives, 300 positives) used for internal validation.
—
Independent dataset: 1,138 cases (1,028 from MIMIC-CXR, 110 from Georgetown University Medical Center)
3 (senior board-certified radiologists)
Indications for Use
ClearRead Xray Pneumothorax is a notification-only triage workflow tool for use by trained professionals to help prioritize chest X-rays. The device operates in parallel to and independent of care image interpretation workflow. Specifically, the device uses an artificial intelligence algorithm to analyze images for features suggestive of a pneumothorax 5 mm or larger; it makes case-level output available to a PACS/workstation or triage. Identification of suspected cases of a pneumothorax is not for diagnostic use beyond notification. ClearRead Xray Pneumothorax is limited to analysis of imaging data as a guide to possible urgency of adult chest X-ray image review, and should not be used in lieu of full patient evaluation or relied upon to make or confirm diagnoses. The device does not replace review and diagnosis of the X-rays by trained professionals. The device is not intended to be used with plain film X-ray.
Device Story
ClearRead Xray Pneumothorax is a triage workflow tool for adult digital frontal chest radiographs. It receives DICOM images via network, processes them using an AI/ML algorithm to detect features suggestive of pneumothorax (≥5 mm), and outputs case-level notifications to PACS/workstations. It operates in parallel to standard clinical workflows. The device includes an image normalization component to remove device-specific characteristics (noise, tone scale, contrast) before analysis. It does not perform ROI segmentation or alter input images. Radiologists use the output to prioritize worklists for urgent review. It does not replace clinical diagnosis or full patient evaluation.
Clinical Evidence
Clinical validation used an independent retrospective dataset of 1,138 adult chest X-rays (>400 true positive PTX, >600 true negative PTX) from MIMIC-CXR and Georgetown University Medical Center. Ground truth was established by a panel of 3 board-certified thoracic radiologists. Primary endpoint was AUC-ROC. Results: AUC 0.974, Sensitivity 0.922, Specificity 0.951, and mean time-to-notification of 9.73 seconds. Comorbidities (atelectasis, pleural effusion, etc.) were represented in the dataset.
Technological Characteristics
Software-based triage tool; DICOM network interface (IEEE 802.3). Normalization component removes device-specific noise/contrast. ML/AI algorithm for pattern detection. Compliant with IEC 62304, IEC 62366-1, and ISO 14971. Moderate level of concern software. No hardware components; standalone software.
Indications for Use
Indicated for use by trained professionals to prioritize adult chest X-rays for suspected pneumothorax (5 mm or larger). Not for diagnostic use; not for plain film X-ray.
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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Riverain Technologies, Inc. % Mr. Jonathan Jackson Director of Regulatory Affairs & Quality Assurance 3020 South Tech Blvd. MIAMISBURG OH 45342
March 10, 2022
# Re: K213566
Trade/Device Name: ClearRead Xray Pneumothorax Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: February 8, 2022 Received: February 10, 2022
Dear Mr. Jackson:
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 (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 located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/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.
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 and Part 809); medical device reporting (reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
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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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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-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,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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#### DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
## Indications for Use
510(k) Number (if known) K213566
Device Name ClearRead Xray Pneumothorax
#### Indications for Use (Describe)
ClearRead Xray Pneumothorax is a notification-only triage workflow tool for use by trained professionals to help prioritize chest X-rays. The device operates in parallel to and independent of care image interpretation workflow. Specifically, the device uses an artificial intelligence algorithm to analyze images for features suggestive of a pneumothorax 5 mm or larger; it makes case-level output available to a PACS/workstation or triage. Identification of suspected cases of a pneumothorax is not for diagnostic use beyond notification. ClearRead Xray Pneumothorax is limited to analysis of imaging data as a guide to possible urgency of adult chest X-ray image review, and should not be used in lieu of full patient evaluation or relied upon to make or confirm diagnoses. The device does not replace review and diagnosis of the X-rays by trained professionals. The device is not intended to be used with plain film X-ray.
Type of Use (Select one or both, as applicable)
| <span style="text-decoration: underline;"></span> | <span style="text-decoration: underline;"></span> |
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#### 5.0 510(K) SUMMARY
| Submission Date: | March 9, 2022 |
|------------------|---------------|
|------------------|---------------|
#### Submitter Information:
| Company Name: | Riverain Technologies, Inc. |
|------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Company Address: | 3020 South Tech Blvd.<br>Miamisburg, OH 45342-4860 |
| Contact Person: | Jonathan Jackson<br>Director, Regulatory Affairs and Quality Assurance<br>Riverain Technologies, Inc.<br>800.990.3387 ext. 5092<br>937.425.6493<br>jjackson@riveraintech.com |
### Device Information:
| Trade Name: | ClearRead Xray Pneumothorax |
|--------------------|-----------------------------------------------------------------|
| Regulation Number: | 21 CFR §892.2080 |
| Regulation Name: | Radiological computer aided triage and notification<br>software |
| Regulatory Class: | Class II |
| Product Code: | QFM |
Device Description: ClearRead Xray Pneumothorax is comprised of a computer assisted triaging tool, designed to prioritize chest X-rays based on the suspected presence of a pneumothorax (PTX) 5mm or larger. ClearRead Xray Pneumothorax requires both lungs to be in the field of view. ClearRead Xray Pneumothorax provides adjunctive information and is not intended to be used for diagnosis. ClearRead Xray Pneumothorax receives images according to the DICOM® protocol (via a standard IEEE 802.3 network connection), processes the image, and delivers the resulting information through the same DICOM network interface. Image inputs are limited to adult, digital frontal chest radiographs. The output results are sent to facilitate prioritization of chest Xrays for radiologist review on one or more devices that
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conform to the ClearRead Xray Pneumothorax DICOM Conformance Statement. ClearRead Xray Pneumothorax does not support printing or DICOM media.
Indications for Use: ClearRead Xray Pneumothorax is a notification-only triage workflow tool for use by trained professionals to help prioritize chest X-rays. The device operates in parallel to and independent of standard of care image interpretation workflow. Specifically, the device uses an artificial intelligence algorithm to analyze images for features suggestive of a pneumothorax 5 mm or larger; it makes case-level output available to a PACS/workstation for worklist prioritization or triage. Identification of cases suspected of containing a pneumothorax is not for diagnostic use beyond notification. ClearRead Xray Pneumothorax is limited to analysis of imaging data as a guide to possible urgency of adult chest X-ray image review and should not be used in lieu of full patient evaluation or relied upon to make or confirm diagnoses. The device does not replace review and diagnosis of the Xrays by trained professionals. The device is not intended to be used with plain film X-ray.
| Predicate Devices: | RADLogics, Inc. |
|--------------------|---------------------|
| | (K193300) |
| | AIMI-Triage CXR PTX |
| | Class II |
# Comparison to Predicate Device Technical Characteristics:
Riverain Technologies, Inc. is of the opinion that ClearRead Xray Pneumothorax is substantially equivalent, both in intended use as well as to the technical characteristics of the listed predicate device. Differences in the design and performance from the cited predicate device does affect either the safety or the effectiveness of ClearRead Xray Pneumothorax for its intended use. Table 1 shows the predicate device listed against the subject device for the Product Code and Intended Use.
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| | Predicate: | Subject Device: |
|----------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | AIMI-Triage CXR PTX<br>(RADLogics, Inc.) | ClearRead Xray<br>Pneumothorax |
| | K193300 | (Riverain Technologies, Inc.)<br>K213566 |
| Product Code | QFM | QFM |
| Intended Use | The AIMI-Triage CXR PTX<br>provides a chest X-ray<br>prioritization service for use<br>by radiologists to identify<br>features suggestive of<br>moderate to large sized<br>pneumothorax. | ClearRead Xray<br>Pneumothorax provides a<br>chest X-ray prioritization<br>service for use by radiologists<br>to identify features suggestive<br>of pneumothoraces in a<br>PA/AP chest x-ray scan. |
| Intended User | Radiologist | Radiologist |
| Modality | X-ray | X-ray |
| Anatomical Region | Lungs | Lungs |
| Clinical Condition | Pneumothorax | Pneumothorax |
| Notification /<br>Prioritization | Yes, passive | Yes, passive |
| ROI Segmentation | No | No |
| Algorithm | Artificial intelligence<br>algorithm with database of<br>images | Machine learning and image<br>processing |
| Alteration of input<br>images | No | No |
Table 1: Predicate Devices vs. Subject Device
# Testing Summary:
# Non-clinical Testing
Non-clinical tests were conducted during the development process in accordance with the Riverain Technologies Design Control Process, which is compliant with the FDA Quality System Regulations, ISO 13485:2016 with MDSAP and the following standards.
- IEC 62304:2006/AMD1:2015, Medical devices Software life cycle processes ●
- EC62366-1:2015, Medical device Part1: Application of usability engineering to ● medical devices
- ISO14971:2007, Medical devices Application of risk management to medical . devices (2nd Ed.)
{6}------------------------------------------------
Testing verified the requirements according to the ClearRead Xray Pneumothorax device specifications. The Risk Management Plan, Risk Analysis and Risk Management Report were completed, and risk control measures were implemented to mitigate the identified hazards. Documentation required for software with a Moderate Level of Concern is included as part of this submission. Device labeling, together with the results from verification and validation testing demonstrate that the device is safe and effective.
# Clinical Performance Testing
Clinical evaluation used an independent dataset, that is data not used for purposes of training or internal validation, to validate that clinical efficacy of ClearRead Xray Pneumothorax for workflow prioritization of X-ray images containing a suspected pneumothorax.
The primary objective of this study was to demonstrate that ClearRead Xray Pneumothorax meets or exceeds the expected performance on an independent test set. Device performance was measured by the AUC of the ROC curve. The primary endpoint was based on an overall assessment of the possible presence of a PTX, without localization.
Retrospective adult (18 and older) patient images from multiple sources were evaluated, including >400 true positive PTX and >600 true negative PTX cases, with approximately equal representation of male and female studies. Truth was determined by a panel of 3 senior board-certified radiologists with expertise in thoracic radiology.
To assess performance, the ClearRead Xray Pneumothorax system was run on all selected images, both true negative and true positives cases. True negative-pneumothorax images that are identified as having a suspected pneumothorax by the system were labeled as "false positives". True positive detections are true positive-pneumothorax images wherein the machine indicates a suspected pneumothorax is present.
Machine indications were transferred to the statistical analysis team and used as the basis for performance assessment, including the generation of ROC, point estimate of the ROC AUC, sensitivity and specificity estimates and associated 95% confidence intervals, and time-to-notification estimates. Device performance, as measured by the AUC-ROC, sensitivity and specificity, and time-to-notification were demonstrated with statistical significance to meet the study's primary endpoints. A summary of the results is listed in Table 2.
| AUC | 0.974 |
|----------------------|--------------|
| Sensitivity | 0.922 |
| Specificity | 0.951 |
| Time to Notification | 9.73 seconds |
Table 2: Clinical Data Summary of Results
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# Clinical Trial Data
Images used for the clinical study originated from MIMIC-CXR and Georgetown University Medical Center. All data used in the clinical trial was independent data, not used as part of product development.
The MIMIC-CXR dataset is a controlled online dataset of chest x-rays images. A second dataset was collected by Georgetown University Medical Center (GUMC) between 2009-2012, also not used as part of the development process.
The MIMIC-CXR cases were collected from 2011 to 2016 at Beth Israel Deaconess Medical Center. A total of 1028 cases from the MIMIC-CXR dataset were selected for the study based on the pre-established protocol inclusion criteria. Of them, there were 419 and 609 with PTX and without PTX, respectively. The remaining cases were provided by Georgetown University and collected between 2002 to 2013. In total, 110 were selected from this dataset. Of them, 40 and 70 were with PTX and without PTX, respectively. Although the header did not contain the manufacturer information for each case for the MIMIC-CXR dataset, it was removed as part of the anonymization process, the administrators of the database did provide a list of the manufacturers, the distribution of the clinical dataset is shown in Table 3.
Data distribution with respect to device characteristics are summarized below:
| Manufacturer | # of cases |
|--------------|------------|
| Carestream | 782 |
| GE | 220 |
| Fuji | 117 |
| Kodak | 12 |
| Agfa | 3 |
| Other | 4 |
| Total | 1138 |
Table 3: Clinical Data Device Distribution
The data distribution with respect to comorbidities are summarize in Table 4 below. Indications of comorbidities for true positive pneumothorax (PTX) cases and true negative pneumothorax (NoPTX) cases are provided:
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| Disease | # of cases | |
|----------------------------|------------|--------|
| | PTX | No PTX |
| Atelectasis | 87 | 108 |
| Cardiomegaly | 30 | 112 |
| Consolidation | 23 | 20 |
| Edema | 15 | 69 |
| Enlarged Cardiomediastinum | 9 | 13 |
| Fracture | 23 | 11 |
| Lung Lesion/mass | 21 | 34 |
| Lung Opacity | 43 | 131 |
| Pleural Effusion | 115 | 114 |
| Pneumonia | 5 | 20 |
Table 4: Clinical Data Comorbidity Distribution
The data distribution with respect to gender is again provided for true positive pneumothorax (PTX) cases and true negative pneumothorax (NoPTX) cases. The gender of a subset of the cases, identified as Unknown, could not be determined and is shown in Table 5.
| | # of cases | |
|---------|------------|--------|
| Gender | PTX | No PTX |
| Male | 227 | 328 |
| Female | 176 | 319 |
| Unknown | 56 | 32 |
Table 5: Clinical Data Gender Distribution
All patients were adult, however specific age was not available due to the anonymization process. Additionally, ethnicity could not be determined as this information is not generally available for image data based on DICOM header content.
# Clinical Data Case Selection and Ground Truth
Three senior expert radiologists formed the Expert Panel, all of which were board certified radiologists with expertise in thoracic radiology. The expert radiologists validated the label of each image as a true positive image or true negative based on the visual inspection of image data along with available radiology reports, and if a true positive case, annotated the location of the pneumothorax with a bounding box.
The members of the expert panel considered each PA/AP image and associated radiology reports independently as part of their review. The final image label and associated annotations were derived from a majority voting rule, where the associated annotation bounding boxes were replaced with a single box that enclosed all bounding boxes.
{9}------------------------------------------------
# Algorithm Development Data
All models used by the ClearRead Xray Pneumothorax system were trained with cases that were clinically validated as negative for pneumothorax, and cases labeled as positive via a simulated data construction process. The simulated cases start with negative cases and digitally insert synthetic pneumothoraces. For synthetic data, the ground truth is arrived at via construction. For real cases the ground truth was hand drawn outlines as established by clinical experts. This included the publicly available data from the NIH, as hosted by Kaggle, wherein experts outlined proven pneumothoraces for thousands of images. Importantly, this also included over 7000 confirmed negatives cases.
Two datasets were constructed for the purposes of developing the pneumothorax system. One dataset, labeled the "development set", was used iteratively to validate and judiciously update the simulation engine, which in turn is used to train the system's models. The second dataset, deemed the test set, was 600 cases, 300 negatives and 300 positives, that were selected based on the diversity of location and size, and were not used in anyway in the development of the models.
Factors associated with manufacturers and patient demographics were not available as the data was thoroughly scrubbed for patient privacy. This was not deemed a limitation as the system utilizes two important aspects that mitigate such concerns. First, the system makes use of a normalization component that removes strong device characteristics such as noise, tone scale and contrast detail. Secondly, by forcing the system to strongly detect local patterns of pneumothorax, the final decision is based on clinically meaningful structure, and not spurious information - age, gender, or ethnicity - as might be learned if just image labels were used. Clinical testing confirms this hypothesis where very similar results were achieved using a large independent dataset.
## Internal Test Data Benchmarks
Figure 1 below provides the image level performance as captured by a receiver operating characteristic (ROC) curve, the area under curve (AUC) was measured to be 0.975 for the internal validation/test set.
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Image /page/10/Figure/0 description: The image is a plot of the true positive rate (TP Rate) versus the false positive rate (FP Rate). The x-axis represents the FP Rate, ranging from 0 to 1, while the y-axis represents the TP Rate, also ranging from 0 to 1. The plot shows a curve that rises sharply from the origin, indicating a high TP Rate for low FP Rates. The curve then flattens out, approaching a TP Rate of 1 as the FP Rate increases.
Figure 1: Internal image level pneumothorax detection performance ROC
For an image-level performance assessment, a probability threshold of 0.5 was selected as the operating point. The performance metrics for the test set at this operating point are found below in Table 6.
| Image<br>Performance | TP | FP | FN | Se | Sp |
|----------------------|-----|----|----|-------|-------|
| Threshold of 0.50 | 278 | 7 | 22 | 92.7% | 97.7% |
Table 6: Image level pneumothorax detection performance indices at the selected operating point
## Conclusion
In preparing this 510(k) submission, Riverain Technologies has carefully considered the relevant statutory and regulatory requirements and believes that the information contained within satisfies the requirements for demonstrating substantial equivalence.
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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.