K221612 · Riverain Technologies, Inc. · OEB · Dec 5, 2022 · Radiology
Device Facts
Record ID
K221612
Device Name
ClearRead CT
Applicant
Riverain Technologies, Inc.
Product Code
OEB · Radiology
Decision Date
Dec 5, 2022
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K221612 · Dec 5, 2022
ClearRead CT
Riverain Technologies, Inc.
Routine clinical CT examinations (patient cohort)
A cohort of 25 patients with 40 real nodules was used to validate the performance of the ClearRead CT Compare nodule matching and registration functions.
Patients with pulmonary nodules undergoing CT examinations; Sample Size: 25 patients (40 real nodules)
Not applicable for this study
Nodule matching accuracy, registration error (distance between post-aligned nodule centroids)
Indications for Use
ClearRead CT is comprised of computer-assisted reading tools designed to aid the radiologist in the detection and characterization of pulmonary nodules during the review of screening and surveillance (low-dose) CT examinations of the chest on a non-oncological patient population. ClearRead CT requires both lungs be in the field of view and is not intended for monitoring patients undergoing therapy for lung cancer or limited field of view CT scans. ClearRead CT provides adjunctive information and is not intended to be used without the original CT series.
Device Story
ClearRead CT Compare is a post-processing software extension to ClearRead CT (K161201). It processes current and prior chest CT series (DICOM input) to identify and match pulmonary nodules across time. The system uses automated detection, volume registration, and nodule matching algorithms to calculate percent volume change and volume doubling time. It operates in a clinical environment, used by radiologists as an adjunctive tool to the original CT series. Output is delivered via DICOM network to storage archives, providing radiologists with quantitative data to track nodule progression. Benefits include improved longitudinal assessment of nodules, aiding clinical decision-making regarding patient surveillance.
Clinical Evidence
Clinical performance evaluated on 25 patients with 42 actionable nodules. 39 nodules had corresponding prior regions; all 39 were correctly matched. 3 new nodules were correctly identified. Average registration error was 4.46mm (SD 2.69mm), within the 15mm tolerance. Non-clinical testing included 900 nodules to validate matching performance (match rates >94% across all types/locations) and residual analysis for vessel suppression on 3.5mm-5mm slice thicknesses.
Technological Characteristics
Software-based medical image management and processing system. Inputs: DICOM thoracic CT series. Features: Vessel suppression, nodule detection, scan registration, nodule matching, and volume measurement. Connectivity: DICOM network interface. Standards: IEC 62304, IEC 62366-1, ISO 14971, NEMA PS 3.1-3-20. Supports slice thicknesses up to 5mm for vessel suppression. Optional micro-nodule filtering (<5mm).
Indications for Use
Indicated for radiologists to aid in detection and characterization of pulmonary nodules in screening and surveillance (low-dose) chest CTs of non-oncological patients. Requires both lungs in field of view. Not for patients undergoing lung cancer therapy or limited field of view scans.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
Lung Nodule Assessment and Comparison Option (LNA) (K162484)
Submission Summary (Full Text)
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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.
December 5, 2022
Riverain Technologies, Inc. % Jonathan Jackson Director of Regulatory Affairs & Quality Assurance 3130 South Tech Blvd. MIAMISBURG OH 45342
## Re: K221612
Trade/Device Name: ClearRead CT Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: OEB, LLZ Dated: October 31, 2022 Received: November 1, 2022
### Dear Jonathan 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/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.
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
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801); medical device 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 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,
2022.12.05
09:08:19
-05'00'
*Lu Jiang*
Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known) K221612
Device Name ClearRead CT
#### Indications for Use (Describe)
ClearRead CT is comprised of computer-assisted reading tools designed to aid the radiologist in the detection and characterization of pulmonary nodules during the review of screening and surveillance (low-dose) CT examinations of the chest on a non-oncological patient population. ClearRead CT requires both lungs be in the field of view and is not intended for monitoring patients undergoing therapy for lung cancer or limited field of view CT scans. ClearRead CT provides adjunctive information and is not intended to be used without the original CT series.
Type of Use (Select one or both, as applicable):
| <span></span> Prescription Use (Part 21 CFR 801 Subpart D) | <span></span> Over-The-Counter Use (21 CFR 801 Subpart C) |
|------------------------------------------------------------|-----------------------------------------------------------|
|------------------------------------------------------------|-----------------------------------------------------------|
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Riverain Technologies Page 5-1
#### 5.0 510(K) SUMMARY
| Submission Date: | December 1, 2022 |
|------------------------|------------------------------------------------------------------------------------------------------------------|
| Submitter Information: | |
| Company Name: | Riverain Technologies, Inc. |
| Company Address: | 3130 South Tech Blvd.<br>Miamisburg, OH 45342-4860 |
| Contact Person: | Jonathan Jackson<br>Director of RAQA<br>Riverain Technologies, Inc.<br>937.531.5092<br>jjackson@riveraintech.com |
#### Device Information:
| Trade Name: | ClearRead CT |
|---------------------|------------------------------------------------|
| Regulation Number: | 21 CFR §892.2050 |
| Regulation Name: | Medical Image Management and Processing System |
| Regulatory Class: | Class II |
| Product Code: | OEB/LLZ |
| Device Description: | ClearRead CT Compare is a post-processing |
Compare is a pos application which processes a prior chest CT to determine whether a nodule detected in the current exam was present in the prior exam using the same detection algorithm used on the current exam. ClearRead CT Compare requires both lungs to be in the field of view. ClearRead CT Compare provides adjunctive information and is not intended to be used without the original CT series and is only invoked on those patients where a prior exam exists and if a nodule is detected in the current exam. ClearRead CT Compare receives images according to the DICOM® protocol, processes the Lung CT series, and delivers the resulting information through the same DICOM network interface in conjunction with results provided for the current exam, specifically whether the nodule is present on the prior exam and if so, the percent volume change between the current and prior exam along with the volume doubling time. Series inputs are limited to Computed Tomography (CT). The ClearRead CT Compare Processor processes each prior series received. The ClearRead CT Compare
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output is sent to a destination device that conforms to the ClearRead CT DICOM Conformance Statement, such as a storage archive. ClearRead CT Compare does not support printing or DICOM media. ClearRead CT Compare is a product extension of our FDA cleared and marketed ClearRead CT device (K161201). The initial device contained ClearRead CT Vessel Suppress as well as ClearRead CT Detect. ClearRead CT (the base system), includes normalization, segmentation, and characterization of nodules, and provides the following key features: ● ClearRead CT Vessel Suppress aids radiologists by suppressing normal structures in the input chest CT series. . ClearRead CT Detect aids radiologists in the detection and characterizations of nodules in the input chest CT series. ● ClearRead CT Compare includes Scan Registration and Nodule Matching functions and adds the following key features: ClearRead CT Compare aids radiologists in ● tracking nodule changes over time, providing additional characterizations per nodule, including percent nodule change and volume doubling time. ClearRead CT is comprised of computer-assisted Indications for Use: reading tools designed to aid the radiologist in the detection and characterization of pulmonary nodules during the review of screening and surveillance (lowdose) CT examinations of the chest on a non-oncological patient population. ClearRead CT requires both lungs be in the field of view and is not intended for monitoring patients undergoing therapy for lung cancer or limited field of view CT scans. ClearRead CT provides adjunctive information and is not intended to be used without the original CT series. Primary Predicate Device: Riverain Technologies, Inc. (K161201) ClearRead CT Class II 21 CFR §892.2050 Secondary Predicate Device: Philips Medical Systems Nederland B.V.
(K162484) Lung Nodule Assessment and Comparison Option (LNA)
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Class II
## Comparison to Predicate Device Technical Characteristics:
Riverain Technologies, Inc. is of the opinion that ClearRead CT Compare is substantially equivalent, both in intended use as well as to the technical characteristics of the listed predicate devices. Differences in the design and performance from the cited predicate device does not affect either the safety or the effectiveness of ClearRead CT Compare for its intended use. Table 5.1 shows the predicate devices listed along with the subject device, including the Product Code as well as the Indications for Use for each device.
| | Primary Predicate:<br>ClearRead CT<br>(Riverain<br>Technologies, Inc.)<br>K161201 | Secondary<br>Predicate:<br>Lung Nodule<br>Assessment and<br>Comparison Option<br>(LNA)<br>(Philips Medical<br>Systems Nederland<br>B.V.) K162484 | Subject Device:<br>ClearRead CT,<br>including<br>ClearRead CT<br>Compare<br>(Riverain<br>Technologies, Inc.) |
|--------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Product<br>Code | OEB/LLZ | LLZ/JAK | OEB/LLZ |
| Indications for<br>Use | ClearRead™ CT is<br>comprised of<br>computer assisted<br>reading tools<br>designed to aid the<br>radiologist in the<br>detection of<br>pulmonary nodules<br>during review of CT<br>examinations of the<br>chest on an<br>asymptomatic<br>population. | The Philips Medical<br>Systems Lung<br>Nodule Assessment<br>and Comparison<br>Option is intended<br>for use as a<br>diagnostic patient-<br>imaging tool.<br>It is intended for the<br>review and analysis<br>of thoracic CT<br>images, providing<br>quantitative and<br>characterizing<br>information about<br>nodules in the lung in<br>a single study, or<br>over the time course<br>of several thoracic<br>studies.<br>Characterizations<br>include diameter | ClearRead CT is<br>comprised of<br>computer-assisted<br>reading tools<br>designed to aid the<br>radiologist in the<br>detection and<br>characterization of<br>pulmonary nodules<br>during the review of<br>screening and<br>surveillance (low-<br>dose) CT<br>examinations of the<br>chest on a non-<br>oncological patient<br>population.<br>ClearRead CT<br>requires both lungs<br>be in the field of<br>view and is not<br>intended for |
| | | volume and volume<br>over time. The<br>system automatically<br>performs the<br>measurements,<br>allowing lung<br>nodules and<br>measurements to be<br>displayed. | monitoring patients<br>undergoing therapy<br>for lung cancer or<br>limited field of view<br>CT scans. ClearRead<br>CT provides<br>adjunctive<br>information and is<br>not intended to be<br>used without the<br>original CT series. |
| Intended User | Radiologist | Radiologists and<br>Technologists | Radiologist |
| Modality | Thoracic CT Series | Thoracic CT Series | Thoracic CT Series |
| Anatomical<br>Region | Chest | Chest | Chest |
| Clinical<br>Condition | Lung Nodules | Lung Nodules | Lung Nodules |
| Nodule Types | Solid, Part-solid,<br>Ground-glass | Solid, Part-solid,<br>Ground-glass,<br>Calcified | Solid, Part-solid,<br>Ground-glass |
| ROI<br>Segmentation | Yes | Yes | Yes |
| Automatic<br>Calculation of<br>Measurements<br>for each<br>Segmented<br>Nodule | Yes | Yes | Yes |
| Temporal<br>Comparison<br>(Nodule<br>Matching) | No | Yes, Semi-Automatic | Yes, Fully Automatic |
| Volume<br>Doubling<br>Time and %<br>Change<br>Calculation | No | Yes | Yes |
| Segmentation<br>of Lung | Yes, Segmentation of<br>the Lungs | Yes | Yes, Segmentation of<br>the Lungs |
| Lungs, and<br>Lung Lobes | | | |
| Alteration of<br>Input Images | No | No | No |
| Average<br>Diameter | No | Yes | Yes |
| Maximum<br>Slice<br>Thickness for<br>Vessel<br>Suppress | 3mm | N/A | 5mm |
| Micro-Nodule<br>Filtering | No | No | Yes |
| Output Objects | Vessel Suppress<br>Series, Detect Series | Interactive Viewing | Vessel Suppress<br>Series, Detect Series,<br>Summary Report |
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## Table 5.1: Predicate Devices vs. Subject Device
Key features identified that differ from the predicate are discussed below.
## Temporal Comparison
The purpose of ClearRead CT Compare is to augment ClearRead CT, without modification, by adding a nodule matching capability. The current scan is processed with the nodule detection algorithm as defined by ClearRead CT. If any regions are detected in the current scan, the system then processes the prior scan using the same automatic detection process. If regions were detected in the prior scan, alignment of the current and prior scans is carried out with a volume registration step.
## Volume Doubling Time and % Change Calculation
Given an ROI from a current scan is matched with an associated ROI from the prior scan, the ClearRead CT Compare engine adds to the output nodule characteristics the percentage volume change and volume doubling time. These added measurements are derived quantities from existing validated measurements, namely the volume estimate of a region within the current and prior scans.
### Average Diameter
The average diameter is derived by taking the average of the minimum and maximum diameters that were measured. These diameters are not new measurements as they were included in the predicate device, ClearRead CT.
### 5mm Slice Thickness for Vessel Suppress
The volume normalization step includes a process for normalizing slice thickness to 1mm. The module works for slice thickness values up to 5mm; however, it has only been utilized for 3mm and less. As there are sites processing thicker slice data, the module for CT scans with slice thickness values within the 3mm to 5mm range was evaluated. The same data that was previously used to assess the performance of vessel suppression for
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slice thickness values ranging from 1mm to 3mm was used to evaluate the extended range. No changes were made to the vessel suppression module to accommodate the thicker slices.
While ClearRead CT may produce a vessel suppressed series for CT scans in the 3mm to 5mm range, the Detect component will not, however, be applied for slice thicknesses greater than 3 mm.
## Micro-Nodule Filtering
ClearRead CT Detect locates nodules that are 5mm in diameter and above, while ClearRead CT Vessel Suppress allows visualization of nodules smaller than 5mm. We added a step to Vessel Suppression that will, optionally, remove objects whose approximate diameter is less than 5mm. This option can be turned on at the site, where the size for defining what constitutes a micro-nodule can be specified. However, the size threshold is not allowed to exceed 5mm to keep the performance of the system in line with existing label and product performance indicators.
In addition to the items listed in Table 5.1, several features and enhancements have been added to ClearRead CT since the initial release. A summary of these is provided below:
- Minor updates to improve performance and throughput ●
- Additional output options and interfaces to facilitate workflow, marker ● verification and 3rd party integrations
- . Administrative and monitoring functions to streamline installations and application health monitoring
### 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 ●
- IEC62366-1:2015, Medical device Part1: Application of usability engineering to ● medical devices
- ISO14971:2007, Medical devices Application of risk management to medical ● devices (2nd Ed.)
- NEMA PS 3.1-3-20, Digital Imaging and Communications in Medicine (DICOM) Set 2016
Testing verified the requirements according to the ClearRead CT device specifications.
The ClearRead CT Risk Analysis for this update was reviewed and updated to Revision H, with risk control measures implemented to mitigate 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.
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Target Nodule Match Rate was set to a minimum of 90% for each selected stratum based on a literature review of existing solutions. The hypotheses tested the performance of the matching routine to ensure that its performance is sufficiently high and that it does not depend on the size, attenuation, or location of the nodule. To this end, detected nodules were split into three categories based on their attenuation pattern: solid, part-solid, and ground glass.
Using a total of 900 nodules for assessment, Tables 5.2 and 5.3 below show ClearRead CT Compare matching performance for each nodule type (solid, part-solid, ground-glass) and each clinical location category (isolated, juxta-vascular, juxta-pleural). Match rate is consistent across nodule type and location, and far exceeds the intended performance benchmark of 90%.
| Nodule Type | Match Rate | Mismatch Rate |
|--------------|---------------------|---------------------|
| Solid | 0.961 (0.952,0.978) | 0.033 (0.027,0.038) |
| Part-solid | 0.957 (0.942,0.971) | 0.039 (0.031,0.044) |
| Ground Glass | 0.946 (0.934,0.965) | 0.040 (0.033,0.046) |
Table 5.2: Nodule Match Performance Estimates by Type.
| Nodule Location | Match Rate | Mismatch Rate |
|-----------------|---------------------|---------------------|
| Isolated | 0.940 (0.934,0.947) | 0.042 (0.035,0.047) |
| Juxta-Vascular | 0.969 (0.963,0.975) | 0.031 (0.024,0.035) |
| Juxta-Pleura | 0.955 (0.949,0.961) | 0.039 (0.033,0.044) |
Table 5.3: Nodule Match Performance Estimates by Location
The computation of the Volume Doubling Time (VDT) and percentage volume change were checked against manual computation to ensure the accuracy of the implemented expression. The manual and automated calculations matched in every instance.
To evaluate the effect of using Vessel Suppress on slices up to 5mm, we used the same data that was previously used to assess the performance of vessel suppression for slice thickness values ranging from 1mm to 3mm. This process, in effect, extends the test. It is important to note that no changes were made to the vessel suppression module to accommodate the thicker slices.
The algorithm was run on 1mm data as a baseline, and across a set of 4 slice thickness values: 3.5, 4.0, 4.5, and 5.0, all in mm. By testing a span of thicker slice values, we ensured that vessel suppression performance is maintained up to 5mm. The data used for evaluation was not used in the development of the vessel suppression module.
Some noise in performance is to be anticipated, but a significant increase in residual is not: 10% was our predefined significance threshold for rejecting a test.
The data from residual analysis can be seen in Tables 5.4 and 5.5 below. As shown, the performance across different slice thickness values had little impact on performance, either for non-contrast or contrast scans.
| | rest of the comments of the comments of the comments of the comments of the contribution of the first of the first of the first of the first of the first of the first of the<br>Slice thickness | Interior Region | Exterior Region |
|--|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|-----------------|
|--|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|-----------------|
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| 3.5 mm | -2.3% | -0.1% |
|--------|-------|-------|
| 4.0 mm | 0.4% | 2.9% |
| 4.5 mm | 1.8% | 2.9% |
| 5.0 mm | -3.7% | 3.8% |
Table 5.4: The average performance for each indicator for Non-Contrast Cases
| Slice thickness | Interior Region | Exterior Region |
|-----------------|-----------------|-----------------|
| 3.5 mm | -2.2% | 1.1% |
| 4.0 mm | -1.5% | 3.3% |
| 4.5 mm | -2.3% | 2.7% |
| 5.0 mm | -4.5% | 4.6% |
Table 5.5: The average performance for each indicator for Contrast Cases
We added a step to Vessel Suppression, micro-nodule filtering, that will, optionally, remove objects whose approximate diameter is less than 5mm. This option can be turned on at the site, where the size for defining what constitutes a micro-nodule can be specified. However, the size threshold is not allowed to exceed 5mm to keep the performance of the system in line with existing label and product performance indicators.
### Clinical Performance Testing
To complement non-clinical testing, 40 real nodules for a 25 patient cohort was assessed. The results are consistent with results derived from simulated data and are summarized below.
Over the 25 cases, a total of 42 actionable nodules were ground-truthed by the radiologist. Of the 42 nodules, 39 were found to have a corresponding region within the prior scan, leaving 3 nodules characterized as new nodules. All 39 nodules pairs were detected and correctly matched, while each of the 3 new ones was detected and correctly identified as new.
The frequency plot below shows the carina offset distance (between current and prior), in mm, for the scans containing real nodules.
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Image /page/11/Figure/2 description: The image is a histogram titled "Real Nodule Cases". The x-axis is labeled "Carina Offset Dist (mm)" and ranges from 0 to 25. The y-axis is labeled "Frequency" and ranges from 0 to 5. The histogram shows the frequency distribution of carina offset distances, with the highest frequency occurring around 5 mm.
Carina offset distances for the real nodule cases
The box plot below shows the local displacement about matched nodules post affine alignment. These distances capture the needed amount of warp, in mm, to align the nodules after subtracting out the potentially large rigid (translational and rotational) effects.
Image /page/11/Figure/5 description: This image is a boxplot titled "Real Nodules". The y-axis is labeled "Warp Dist (mm)" and ranges from 0 to 30. The boxplot shows the distribution of the data, with the median around 12, the interquartile range between 6 and 18, and the whiskers extending to approximately 1 and 28.
Length of the warp required to align nodule centers, in mm, over the real cases
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The registration, or alignment error (distance between post aligned nodule centroids), of each of the 39 real nodules successfully matched in the prior scan was performed. The figure below shows the distribution of error over all nodules.
Image /page/12/Figure/3 description: The image is a bar graph that shows the frequency of nodule registration errors. The x-axis represents the error distance in millimeters, ranging from 0 to 16. The y-axis represents the frequency, ranging from 0 to 14. The bar graph shows that the most frequent error distance is 4 mm, with a frequency of approximately 13.
Nodule Registration Error
Distribution of Nodule Registration Errors for Real Nodules
The average registration error was 4.46mm, with an associated standard deviation of 2.69mm. As shown, most nodules are aligned with high precision, well within our predefined 15mm tolerance.
ClearRead CT Compare is an extension of an existing, approved medical device. Nodule detection performance and measurements were validated as part of the initial submission of ClearRead CT (K161201). The current and prior scans are analyzed independently using the already cleared ClearRead CT Detect application.
## 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 in terms of design features, fundamental technology, indications for use, and the safety and effectiveness of the device. Additionally, verification and validation testing demonstrate the safety and efficacy of the device to meet its intended use and specifications when operated as intended, with no detrimental impact upon the benefit / risk ratio of the device.
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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.