(184 days)
V5med Lung AI is a Computer-Aided Detection (CAD) software designed to assist radiologists in detecting pulmonary nodules (with diameter of 4-30 mm) during CT examinations of the chest for asymptomatic populations. This software provides adjunctive information to alert radiologists to regions of interest with suspected lung nodules that may otherwise be overlooked. It can be used in a concurrent read mode, where the AI analysis results are displayed alongside the original CT images during either the initial review or any subsequent reviews by the radiologist. V5med Lung AI does not replace the radiologist's critical judgment or diagnostic processes and should not be used in isolation from the original CT series.
The V5med Lung AI is a software product designed to detect nodules in the lungs. The detection model is trained using a Deep Convolutional Neural Network (CNN) based algorithm, enabling automatic detection of lung nodules ranging from 4 to 30 mm in chest CT images.
The system integrates algorithm logic and database on the same server, ensuring simplicity and ease of maintenance. It accepts chest CT images from a PACS system, Radiological Information System (RIS), or directly from a CT scanner, analyzes the images, and provides output annotations regarding lung nodules.
This document describes the regulatory acceptance criteria met by the V5med Lung AI device and the study conducted to prove its performance.
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for the V5med Lung AI device are implicitly set by the endpoints measured in the clinical performance evaluation, which aimed to demonstrate improved radiologist performance with the AI tool compared to unaided reads. The reported device performance directly addresses these implicit criteria.
| Metric | Acceptance Criteria (Implicit) | Reported Device Performance (Aided vs Un-aided) | Difference (95% CI) | Result |
|---|---|---|---|---|
| AUC (Localization-Specific ROC) | Significant increase in AUC with aid. | Unaided: 0.734, Aided: 0.830 | 0.0959 (0.0586, 0.1332) | Met (Significant increase, CI entirely above 0) |
| Reading Times (seconds) | Significant decrease in reading time with aid. | Unaided: 133.0, Aided: 115.9 | -17.1 (-26.7, -9.0) | Met (Significant decrease, CI entirely below 0) |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 340 chest CT scans.
- Data Provenance:
- Country of Origin: Not explicitly stated, but all screening cases were acquired from the NLST (National Lung Screening Trial) CT arm, implying a US-based origin.
- Retrospective or Prospective: Retrospective. The study was a "retrospective, fully crossed, multi-reader multi-case (MRMC) study."
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not explicitly state the number of experts used to establish the ground truth for the test set. It mentions sixteen board-certified radiologists participated in the reader study. These radiologists were involved in reading the cases with and without the AI aid, and their performance served as the basis for evaluating the AI's effectiveness.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method for establishing the ground truth for the test set. It mentions a "fully crossed, multi-reader multi-case (MRMC) study" design, where sixteen radiologists read the cases. This implies that the performance metrics (AUC, reading times) were derived from their individual interpretations, likely compared against a pre-established consensus ground truth, though the method for that consensus is not detailed.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size
- Yes, a multi-reader multi-case (MRMC) comparative effectiveness study was done.
- Effect Size of Human Readers Improvement with AI vs. Without AI Assistance:
- AUC: Radiologists using V5med Lung AI showed a 0.0959 increase in AUC (from 0.734 unaided to 0.830 aided). The 95% confidence interval for this difference was (0.0586, 0.1332), indicating a statistically significant improvement.
- Reading Times: Radiologists using V5med Lung AI showed a 17.1-second decrease in reading time (from 133.0 seconds unaided to 115.9 seconds aided). The 95% confidence interval for this difference was (-26.7, -9.0), indicating a statistically significant reduction. This translates to approximately a 13% improvement in reading time.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
- Yes, standalone performance testing was conducted.
- The document states: "Standalone performance testing, which included chest CT scans from lung cancer screening population and non-screening population, was conducted to validate detection accuracy of V5med Lung AI."
- It further notes: "Results showed that V5med Lung AI had similar nodule detection sensitivity compared to those of the predicate device."
7. The Type of Ground Truth Used
The document does not explicitly define the type of ground truth used for the standalone performance testing or the MRMC study's assessment of radiologist performance. However, given the context of lung nodule detection in oncology, common ground truth methods include:
- Expert Consensus: Multiple expert radiologists review cases and reach a consensus on the presence and characteristics of nodules. This is a very common method for CAD device validation. The prompt indicates "consensus" as a possibility.
- Pathology: Biopsy results confirming the nature of lesions, though this is often not feasible for all identified nodules, especially in large screening datasets.
- Outcomes Data: Longitudinal follow-up of patients to see if nodules grow or are confirmed to be malignant over time.
Given the NLST data source, it is highly probable that the ground truth was established through a rigorous process, likely involving expert consensus and potentially correlation with long-term follow-up from the trial, but the specific details are not provided in this excerpt.
8. The Sample Size for the Training Set
The document does not provide the sample size for the training set. It only states that the detection model was "trained using a Deep Convolutional Neural Network (CNN) based algorithm."
9. How the Ground Truth for the Training Set Was Established
The document does not describe how the ground truth for the training set was established. It only mentions the use of a Deep Convolutional Neural Network (CNN) based algorithm for training the detection model.
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image shows the logo for the U.S. Food & Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in a stacked format. The logo is simple and professional, and it is easily recognizable.
March 27, 2025
V5med Inc. % Ining Cheng Consultant 7F., No. 36, Chenggong 12th St., Zhubei City Hsinchu County, 302050 TAIWAN
Re: K242919
Trade/Device Name: V5med Lung AI Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OEB, LLZ Dated: February 24, 2025 Received: February 24, 2025
Dear Ining Cheng:
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.
{1}------------------------------------------------
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).
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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about 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-regulatory
{2}------------------------------------------------
assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
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
Enclosure
{3}------------------------------------------------
Indications for Use
Submission Number (if known)
K242919
Device Name
V5med Lung Al
Indications for Use (Describe)
V5med Lung AI is a Computer-Aided Detection (CAD) software designed to assist radiologists in detecting pulmonary nodules (with diameter of 4-30 mm) during CT examinations of the chest for asymptomatic populations. This software provides adjunctive information to alert radiologists to regions of interest with suspected lung nodules that may otherwise be overlooked. It can be used in a concurrent read mode, where the Al analysis results are displayed alongside the original CT images during either the initial review or any subsequent reviews by the radiologist. V5med Lung Al does not replace the radiologist's critical judgment or diagnostic processes and should not be used in isolation from the original CT series.
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)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
{4}------------------------------------------------
Image /page/4/Picture/1 description: The image shows the logo for V5med. The logo is in blue and features a stylized "V" and "5" connected together. The "5" has a curved line extending from it, resembling a wave or a swoosh. The word "med" is written in lowercase letters to the right of the "V5" symbol, also in blue.
510(k) Summary
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of 21 CFR 807.87(h).
| The assigned 510(k) number is | K242919 |
|---|---|
| Submitter: | V5med Inc. |
| 7F., No. 36, Chenggong 12th St., Zhubei City, | |
| Hsinchu County, Taiwan | |
| Phone: +886-3-623-3089 | |
| Contact Person | JONI CHENG |
| Email: Joni.Cheng@v5.com.tw | |
| Phone: +886-3-623-3089 | |
| Date Prepared | March 26, 2025 |
| Device Name | V5med Lung AI |
| Regulation Name: | Medical image management and processing system |
| CFR Classification: | 21 CFR 892.2050 |
| Device Class | II |
| Product Code: | OEB, LLZ |
| Panel: | Radiology |
| Primary Predicate device: | AVIEW Lung Nodule CAD (K221592) |
| Secondary Predicate device: ClearRead CT (K161201) |
{5}------------------------------------------------
Image /page/5/Picture/1 description: The image shows the logo for V5med. The logo is written in a sans-serif font and is blue. The number 5 in the logo has a curved line extending from the bottom of the number. The text "med" is written to the right of the number 5.
Indications for use
V5med Lung AI is a Computer-Aided Detection (CAD) software designed to assist radiologists in detecting pulmonary nodules (with diameter of 4-30 mm) during chest CT examinations of asymptomatic populations. This software provides adjunctive information to alert radiologists to regions of interest with suspected lung nodules that may otherwise be overlooked. It can be used in a concurrent read mode, where the AI analysis results are displayed alongside the original CT images during either the initial review or any subsequent reviews by the radiologist. V5med Lung AI does not replace the radiologist's clinical judgment or diagnostic processes and should not be used in isolation from the original CT series.
Device Description
The V5med Lung AI is a software product designed to detect nodules in the lungs. The detection model is trained using a Deep Convolutional Neural Network (CNN) based algorithm, enabling automatic detection of lung nodules ranging from 4 to 30 mm in chest CT images.
The system integrates algorithm logic and database on the same server, ensuring simplicity and ease of maintenance. It accepts chest CT images from a PACS system, Radiological Information System (RIS), or directly from a CT scanner, analyzes the images, and provides output annotations regarding lung nodules.
Summary of the technological characteristics
V5med Lung AI is functionally equivalent to the following predicate device: AVIEW Lung Nodule CAD (K221592) cleared February 24, 2023.
The following table demonstrates that the intended uses and technical characteristics of V5med Lung AI are substantially equivalent to the predicate devices.
Any differences between the subject device and the predicated device has no negative impact on safety or efficacy of the subject device and does not raise any new potential safety risks and is equivalent in performance to existing legally marketed devices.
{6}------------------------------------------------
Image /page/6/Picture/1 description: The image shows the logo for V5med. The logo is blue and features the text "V5med" in a stylized font. The "V" and "5" are connected, and the "5" has a curved line extending from its bottom, resembling a smile. The word "med" is written in a smaller font size compared to "V5".
Functional Specification Comparison Table for the V5med Lung AI and AVIEW Lung Nodule CAD (K221592):
| Specification | Subject Device | Primary Predicate DeviceK221592 | Secondary Predicate DeviceK161201 | Comparison |
|---|---|---|---|---|
| Device Name | V5med Lung AI | AVIEW Lung Nodule CAD | ClearRead CTTM | N/A |
| Classification Name | Medical Image Management andProcessing System | Medical Image Management andProcessing System | Medical image management andprocessing system | Same |
| Device Class | Class II | Class II | Class II | Same |
| Regulation Number | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.2050 | Same |
| Product Code | OEB/LLZ | OEB/LLZ | OEB/LLZ | Same |
| Review Panel | Radiology | Radiology | Radiology | Same |
| 510(k) Number | K242919 | K221592 | K161201 | N/A |
| Indications for Use | V5med Lung AI is a Computer-AidedDetection (CAD) software designed toassist radiologists in detectingpulmonary nodules (with diameter of4-30 mm) during CT examinations ofthe chest for asymptomaticpopulations. This software providesadjunctive information to alertradiologists to regions of interest withsuspected lung nodules that mayotherwise be overlooked. It can beused in a concurrent read mode, wherethe AI analysis results are displayedalongside the original CT imagesduring both the initial review and anysubsequent reviews by the radiologist.V5med Lung AI does not replace theradiologist's critical judgment ordiagnostic processes and should notbe used in isolation from the originalCT series. | AVIEW Lung Nodule CAD is aComputer-Aided Detection (CAD)software designed to assistradiologists in the detection ofpulmonary nodules (with diameter 3-20 mm) during the review ofCT examinations of the chest forasymptomatic populations. AVIEWLung Nodule CAD providesadjunctive information to alert theradiologists to regions of interestwith suspected lung nodules that mayotherwise be overlooked. AVIEWLung Nodule CAD may be used as asecond reader after the radiologisthas completed their initial read. Thealgorithm has been validated usingnon-contrast CT images, the majorityof which were acquired on SiemensSOMATOM CT series scanners;therefore, limiting device use to usewith Siemens SOMATOM CT seriesis recommended. | ClearRead CTTM is comprised ofcomputer assisted reading toolsdesigned to aid the radiologist in thedetection of pulmonary nodulesduring review of CT examinations ofthe chest on an asymptomaticpopulation. The ClearRead CTrequires both lungs be in the field ofview. ClearRead CT providesadjunctive information and is notintended to be used without theoriginal CT series. | V5med Lung AI and AVIEW LungNodule CAD (predicate device) areboth Computer-Aided Detection (CAD)software designed to assist radiologistsin detecting pulmonary nodules duringCT examinations of the chest forasymptomatic populations. Bothprovide adjunctive information to alertradiologists to regions of interest withsuspected lung nodules that mayotherwise be overlooked.However, V5med Lung AI supports abroader nodule diameter range (4-30mm), compared to AVIEW's range of3-20 mm, making it similar toClearRead CTTM, which also coversnodules from 5-20 mm. Additionally,V5med Lung AI does not restrict theuse to non-contrast images, unlikeAVIEW, aligning it more closely withClearRead CTTM. Furthermore, V5medLung AI does not limit the scannersource, while AVIEW is validated |
{7}------------------------------------------------
Image /page/7/Picture/1 description: The image shows the logo for V5med. The logo is in blue and features a stylized "V" and "5" connected together. The word "med" is written in lowercase letters to the right of the "V5".
| General Description | ||||
|---|---|---|---|---|
| The V5med Lung AI is a software product designed to detect nodules in the lungs. The detection model is trained using a Deep Convolutional Neural Network (CNN) based algorithm, enabling automatic detection of lung nodules ranging from 4 to 30 mm in chest CT images. The system integrates algorithm logic and database on the same server, ensuring simplicity and ease of maintenance. It accepts chest CT images from a Picture Archiving and Communication System (PACS), Radiological Information System (RIS), or directly from a CT scanner, analyzes the images, and provides output annotations regarding lung nodules. | The AVIEW Lung Nodule CAD is a software product that detects nodules in the lung. The lung nodule detection model was trained by Deep Convolution Neural Network (CNN) based algorithm from the chest CT image. Automatic detection of lung nodules of 3 to 20mm in chest CT images. By complying with DICOM standards, this product can be linked with the Picture Archiving and Communication System (PACS) and provides a separate user interface to provide functions such as analyzing, identifying, storing, and transmitting quantified values related to lung nodules. The CAD's results could be displayed after the user's first read, and the user could select or de-select the mark provided by the CAD. The device's performance was validated with SIEMENS' SOMATOM series manufacturing. The device is intended to be used with a cleared AVIEW platform. | ClearRead CT is a dedicated post-processing application that generates a secondary vessel suppressed Lung CT series with CADe marks and associated region descriptors intended to aid the radiologist in the detection of pulmonary nodules. | primarily with Siemens SOMATOM CT series scanners, making V5med Lung AI more flexible like ClearRead CTTM. These differences do not impact the safety and effectiveness of V5med Lung AI.V5med Lung AI and AVIEW Lung Nodule CAD both utilize Deep Convolutional Neural Network (CNN) based algorithms for lung nodule detection in chest CT images. However V5med Lung AI supports a broader nodule detection range of 4 to 30 mm, compared to AVIEW's 3 to 20 mm, making it similar to ClearRead CT™™, which also covers nodules from 5 - 20 mm. Unlike AVIEW, which is validated with SIEMENS' SOMATOM series and intended to be used with a cleared AVIEW platform, V5med Lung AI does not restrict the scanner source or the use of non-contrast images, aligning it more closely with ClearReac CT™™ in terms of flexibility. These differences do not impact the safety and effectiveness of V5med Lung AI. | |
| Detection target(s) | Pulmonary nodules in screening and diagnostic chest CT acquisitions. | Pulmonary nodules in non-contrast chest CT acquisitions | Pulmonary nodules in chest CT acquisitions. | The detection targets of V5med Lung AI are similar to the detection targets of the predicate devices. |
| Nodule Characteristics | Diameter: | Diameter: | Diameter: | Diameter: |
| • Pulmonary nodules 4 – 30 mm | • Pulmonary nodules 3 – 20 mm | • Pulmonary nodules 5 – 20 mm | The diameter range of V5medLung AI is similar to thepredicate devices | |
| Locations:• Full range: central, peripheral | Locations:• Full range: central, peripheralContours:• Round, irregular | Locations:• Same as primary predicate | ||
| Nodule Marking | A bounding box is provided aroundnodules | A bounding box is provided aroundnodules | A bounding box is provided aroundnodules | Same |
| Automatically locateand identify lungnodules | Yes | Yes | Yes | Same |
| Modifies the OriginalCT scan | No | No | Yes | Same as AVIEW Lung Nodule CAD. |
| Image format | DICOM | DICOM | DICOM | Same |
| Hosting Platform | - | AVIEW | - | No specific hosting platform. Same ascleared ClearRead CT. |
| Hosting Application | - | AVIEW LCS | - | No specific hosting application. Sameas cleared ClearRead CT. |
| Outputs | DICOM GSPS (Grayscale SoftcopyPresentation State) | DICOM GSPS (Grayscale SoftcopyPresentation State)XML (Coordinate of detectednodules)Able to view resultson AVIEW, AVIEWLCS viewer page | DICOM GSPS (Grayscale SoftcopyPresentation State) | Similar to AVIEW Lung Nodule CADbut lacks XML output and specificviewer capabilities of AVIEW.The outputs of V5med Lung AI aresimilar to the outputs of the predicatedevices. |
| Type of Scans | CT | CT | CT | Same |
| CT Scanners | Multi-vendor and multi-detectorCT (MDCT) scanners(Siemens, GE, Philips, andToshiba) | Siemens SOMATOM CTScanners | Not Provided | V5med Lung AI does not restrict thebrand or specifications of the scanner,same as ClearRead CT. |
{8}------------------------------------------------
Image /page/8/Picture/1 description: The image shows the logo for V5med. The logo is blue and features the text "V5med". The "5" in the logo has a curved line extending from the bottom of the number.
{9}------------------------------------------------
Image /page/9/Picture/1 description: The image shows the logo for V5med. The logo is in blue and features the text "V5med" in a stylized font. The "V5" is connected, and there is a curved line that goes from the bottom of the "V" to the top of the "5". The "med" is in a smaller font and is connected to the "5".
Non-Clinical performance evaluation
Software testing was performed in accordance with General Principles of Software Validation: Final Guidance for Industry and FDA Staff (January 11, 2002), Guidance for the Content of Premarket Submissions for Device Software Functions. (June 14, 2023) and IEC 62304:2006/Amd 1:2015 - Medical device software-software life cycle processes. Software testing, including unit testing, software integration testing and software system testing, was conducted on V5med Lung AI. The results demonstrated that V5med Lung AI, when used according to its operating instructions, met all requirement specifications. All system functionalities were tested and passed. Measurement performance was validated on phantom and clinical data to assess reproducibility and accuracy.
Standalone performance testing, which included chest CT scans from lung cancer screening population and non-screening population, was conducted to validate detection accuracy of V5med Lung AI. Results showed that V5med Lung AI had similar nodule detection sensitivity compared to those of the predicate device.
Software Verification and Validation
Unit Test
Conducting unit tests using Jest on major software components identified by the software development team. These tests focus on ensuring the correctness of individual API endpoints and their interactions with the ORM framework, including data retrieval, manipulation, and validation.
System Test
In accordance with the 'integration Test Cases' discussed in advance by the software development team and test team, the system test was conducted by installing software to hardware with recommended system specifications. Any new software errors discovered by the 'Exploratory Test' conducted by the test team were documented and managed as new test cases following discussion between the development and test teams. Discovered software errors were categorized into 3 severity levels for further management.
- ✔ Major defects: Impact the product's intended use with workaround unavailable.
- ✔ Moderate defects: Typically related to user interface or general quality of product, but have available workarounds.
- ✔ Minor defects: Do not impact the product's intended use and are Not significant.
The success standard for the System Test was the absence of 'Major' and 'Moderate' defects which were successfully achieved in the test.
Page 6/7
{10}------------------------------------------------
Image /page/10/Picture/1 description: The image shows the logo for V5med. The logo is in blue and features a stylized "V5" followed by the word "med" in a smaller font size. The "5" in "V5" has a curved line extending from its bottom, creating a visual element that resembles a wave or a stylized medical symbol.
Clinical performance evaluation
A pivotal reader study was conducted through a retrospective, fully crossed, multi-reader multi-case (MRMC) study to validate that the device conforms to the defined user needs and intended uses. A total of sixteen board-certified radiologists and a dataset of 340 chest CT scans were involved in the reader study including both screening and non-screening populations.
All screening cases were acquired from NLST CT arm. The range of participating patients age was between 55 and 77 (mean age was 62). Male to female patient ratio was 41.1% verse 58.9%. Percent race distribution of white, black, Asian, pacific islander, and Indian were 92.8%, 4.4%, 2.2%, 0.3% and 0.3%, respectively.
The purpose of the reader study was to test a hypothesis that with the aid of V5med Lung AI, radiologists' nodule detection performance would be improved at a significance level alpha of 0.05 (two-sided). The reader study measured the area under the curve (AUC) of the localization-specific receiver operating characteristic (LROC) response when using V5med Lung AI, compared to the unaided read. Radiologist interpretation times were also analysed.
Readers benefited from using V5med Lung AI, demonstrating a significant increase in the AUC (Aided-Unaided: 0.0959, 95%CI: (0.0586, 0.1332)) and a significant decrease in reading time from 133.0 seconds unaided to 115.9 seconds, vielding a time difference (Aided-Unaided: - 17.1, 95% CI: (-26.7, -9.0)), a 13% improvement. All endpoints met the acceptance criteria. In summary, the study indicated that radiologist performance with the V5med Lung AI is superior to the unaided read for detecting nodules. Additionally, V5med Lung AI was found to reduce read times with and without outliers. Since V5med Lung AI has the same intended use and no differences in technological characteristics that raise safety or effective concerns, it demonstrates substantial equivalence to the predicate device.
| Unaided | Aided | Difference (95%CI) | |
|---|---|---|---|
| AUC | 0.734 | 0.830 | 0.0959 (0.0586, 0.1332) |
| Reading Times (s) | 113.0 | 115.9 | -17.1 (-26.7, -9.0) |
Conclusion (Substantial Equivalence Conclusions)
Based on the comparison of indications for use, technological features, and performance testing result, V5med Lung AI is found to be as safe and effective as the predicate device. It has been demonstrated to be substantially equivalent to the predicate device.
§ 892.2050 Medical image management and processing system.
(a)
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.(b)
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).