Prostate MR AI is a plug-in Radiological Computer Assisted Detection and Diagnosis Software device intended to be used · with a separate hosting application · as a concurrent reading aid to assist radiologists in the interpretation of a prostate MRI examination acquired according to the PI-RADS standard · in adult men (40 years and older) with suspected cancer in treatment naïve prostate glands. The plug-in software analyzes non-contrast T2 weighted (T2W) and diffusion weighted image (DWI) series to segment the prostate gland and to provide an automatic detection and segmentation of regions suspicious for cancer. For each suspicious region detected, the algorithm moreover provides a lesion Score, by way of PI-RADS interpretation suggestion. Outputs of the device should be interpreted consistently with ACR recommendations using all available MR data (e.g., dynamic contrast enhanced images [if available]). Patient management decisions should not be made solely based on analysis by the Prostate MR AI algorithm.
Device Story
Prostate MR AI is a plug-in software for radiological workstations that assists radiologists in detecting and characterizing suspicious prostate lesions. It takes non-contrast T2-weighted (T2W) and diffusion-weighted (DWI) MRI series as input. Using an AI/ML algorithm, it performs automatic segmentation of the prostate gland (including peripheral and non-peripheral zones) and identifies suspicious regions. The device outputs a suspicion map, lesion contours, a severity score (3-5), and a granular Level of Suspicion (LoS) score (60-100). Radiologists use these outputs within a hosting application to confirm, reject, or edit findings. The device acts as a concurrent reading aid; it does not replace clinical judgment. By providing automated detection and standardized scoring, it aims to improve diagnostic accuracy and inter-reader agreement, potentially leading to more consistent identification of clinically significant prostate cancer.
Clinical Evidence
A multi-reader multi-case (MRMC) study with 12 board-certified radiologists evaluated 340 cases. Primary endpoint: case-level diagnostic performance (AUROC) for discriminating Gleason Grade Group ≥1. In the fully inclusive analysis, AUROC improved from 0.676 (unaided) to 0.701 (aided) (p=0.040). In the maximally restrictive analysis, AUROC improved from 0.658 to 0.695 (p=0.006). Secondary endpoint: lesion-level AUwAFROC also showed statistically significant improvement. Inter-reader agreement (Fleiss' Kappa) significantly improved from 0.283 to 0.371 (p<0.0001). Non-clinical testing confirmed prostate segmentation Dice score >0.9 and lesion detection sensitivity ≥0.80.
Technological Characteristics
Software-only device; AI/ML algorithm trained on PI-RADS standard prostate MRI (T2W and DWI). Performs automatic segmentation and lesion classification. Operates as a plug-in to a hosting application. Complies with ISO 14971, IEC 62304, and IEC 82304-1. No hardware components; no PHI storage. Connectivity via DICOM-compliant hosting workflow.
Indications for Use
Indicated for adult men (40+ years) with suspected cancer in treatment-naïve prostate glands undergoing prostate MRI. Used as a concurrent reading aid to assist radiologists in interpreting PI-RADS standard MRI examinations.
Regulatory Classification
Identification
A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.
Special Controls
A radiological computer assisted detection and diagnosis software must comply with the following special controls: Design verification and validation must include: 1. i. A detailed description of the image analysis algorithm, including but not limited to a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output 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 improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable. iii. Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contain a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment. iv. Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of but not limited to clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in 1(iii) above. v. Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures. 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 device instructions for use, including the intended reading protocol and how the user should interpret the device output. iii. A detailed description of the intended user, and any user training materials as programs that addresses appropriate reading protocols for the device to ensure that the end user is fully aware of how to interpret and apply the device output. iv. A detailed description of the device inputs and outputs. v. A detailed description of compatible imaging hardware and imaging protocols. vi. Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality or for certain subpopulations), as applicable. vii. A detailed summary of the performance testing, including: test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, 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 image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output 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 improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
*e.g.,* receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
*e.g.,* receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(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 device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
*e.g.,* poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
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March 5, 2025
Siemens Healthcare GmbH Abhineet Johri Regulatory Affairs Manager Henkestr. 127 Erlangen, 91052 Germany
Re: K241770
Trade/Device Name: Prostate MR AI (VA10A) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: ODO Dated: February 6, 2025 Received: February 6, 2025
Dear Abhineet Johri:
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.
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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.
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For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
D.R.K.
Daniel M. Krainak, Ph.D Assistant Director Magnetic Resonance and Nuclear Medicine Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices 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) K241770
Device Name Prostate MR AI (VA10A)
Indications for Use (Describe)
Prostate MR AI is a plug-in Radiological Computer Assisted Detection and Diagnosis Software device intended to be used · with a separate hosting application
· as a concurrent reading aid to assist radiologists in the interpretation of a prostate MRI examination acquired according to the PI-RADS standard
· in adult men (40 years and older) with suspected cancer in treatment naïve prostate glands
The plug-in software analyzes non-contrast T2 weighted (T2W) and diffusion weighted image (DWI) series to segment the prostate gland and to provide an automatic detection and segmentation of regions suspicious for cancer. For each suspicious region detected, the algorithm moreover provides a lesion Score, by way of PI-RADS interpretation suggestion.
Outputs of the device should be interpreted consistently with ACR recommendations using all available MR data (e.g., dynamic contrast enhanced images [if available]).
Patient management decisions should not be made solely based on analysis by the Prostate MR AI algorithm.
| Type of Use (Select one or both, as applicable) |
|-------------------------------------------------|
|-------------------------------------------------|
| × Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
|------------------------------------------------|---------------------------------------------|
|------------------------------------------------|---------------------------------------------|
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### 510(k) Summary K241770 Prostate MR AI (VA10A)
In accordance with 21 CFR §807.92, the following summary of safety and effectiveness is provided.
### SUBMITTER I.
21CFR § 807.92(a)(1)
21CFR § 807.92(a)(3)
Siemens Healthcare GmbH Henkestr. 127 91052 Erlangen Germany
Contact: Mr. Abhineet Johri Phone: +1 (484) 680-8723 Email: abhineet.johri@siemens-healthineers.com
Date Prepared: May 17, 2024
### DEVICE II.
21CFR § 807.92(a)(2) Device Trade Name Prostate MR AI (VA10A) Classification Name Radiological Computer Assisted Detection/Diagnosis Software For Lesions Suspicious For Cancer Device Classification Panel Radiology 892.2090 Regulation Number Product Code QDQ
### III. LEGALLY MARKETED PREDICATE DEVICES
| Predicate Device | |
|----------------------------------------------------------------|------------|
| Device Trade Name | Transpara™ |
| 510(k) Number | K181704 |
| Regulation Number | 892.2090 |
| Product Code | QDQ |
| This predicate has not been subject to a design-related recall | |
### Reference Device
ProstatID™M Device Trade Name
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| 510(k) Number | K212783 |
|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|
| Regulation Number | 892.2090 |
| Product Code | QDQ |
| MINIMAL A SECTION A CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION CONSULTION | |
This predicate has not been subject to a design-related recall.
### IV. DEVICE DESCRIPTION SUMMARY
21CFR § 807.92(a)(4)
This premarket notification addresses the Siemens Healthineers Prostate MR AI (VA10A) Radiological Computer Assisted Detection and Diagnosis Software (CADe/CADx).
Prostate MR AI is a Computer Assisted Detection and Diagnosis algorithm designed to plug into a hosting workflow that assists radiologists in the detection of suspicious lesions and their classification. It is used as a concurrent reading aid to assist radiologists in the interpretation of a prostate MRI examination acquired according to the PI-RADS standard.
The automatic lesion detection requires transversal T2W and DWI series as inputs. The device automatically exports a list of detected prostate regions that are suspicious for cancer (each list entry consists of contours and a classification by Score and Level of Suspicion (LoS)), a computed suspicion map, and a per-case LoS. The results of the Prostate MR AI plug-in (with the case-level LoS, lesion center points, lesion diameters, lesion ADC median, lesion 10th percentile, suspicion map, and non-PZ segmentation considered optional) are to be shown in a hosting application that allows the radiologist to view the original case, as well as confirm, reject, or edit lesion candidates with their contours and Scores as generated by the Prostate MR AI plug-in. Moreover, the radiologist can add lesions with contours and PI-RADS scores and finalize the case. In addition, the outputs include an automatically computed prostate segmentation, as well as sub-segmentations of the peripheral zone and the rest of the prostate (non-PZ).
The algorithm will augment the prostate workflow of currently cleared syngo.MR General Engine if activated via a separate license on the General Engine.
### INTENDED USE/INDICATIONS FOR USE V.
21CFR § 807.92(a)(5)
| Predicate Device<br>Transpara™<br>K181704 | Reference Device<br>ProstatID™<br>K212783 | Subject Device<br>Prostate MR AI (VA10A) |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| The ScreenPoint Transpara™<br>system is intended for use as a<br>concurrent reading aid for<br>physicians interpreting screening<br>mammograms, to identify<br>regions suspicious for breast<br>cancer and assess their<br>likelihood of malignancy.<br>Output of the device includes<br>marks placed on suspicious soft<br>tissue lesions and suspicious<br>calcifications; region-based<br>scores, displayed upon the<br>physician's query, indicating the | ProstatID™ is a radiological<br>computer assisted detection<br>(CADe) and diagnostic (CADx)<br>software device for use in a<br>healthcare facility or hospital to<br>assist trained radiologists in the<br>detection, assessment and<br>characterization of prostate<br>abnormalities, including cancer<br>lesions using MR image data | Prostate MR AI is a plug-in<br>Radiological Computer Assisted<br>Detection and Diagnosis<br>Software device intended to be<br>used<br>• with a separate hosting<br>application<br>• as a concurrent reading aid<br>to assist radiologists in the<br>interpretation of a prostate<br>MRI examination acquired |
| likelihood that cancer is present | with the following indications<br>for use. | according to the PI-RADS<br>standard |
| in specific regions; and an | ProstatID analyzes T2W, DWI<br>and ADC MRI data. ProstatID<br>does not include DCE images in<br>its analysis. | in adult men (40 years and<br>older) with suspected cancer<br>in treatment naïve prostate<br>glands |
| overall score indicating the | ProstatID software is intended<br>for use as a concurrent reading<br>aid for physicians interpreting<br>prostate MRI exams of patients<br>presented for high-risk screening<br>or diagnostic imaging, from<br>compatible MRI systems, to<br>identify regions suspicious for<br>prostate cancer and assess their<br>likelihood of malignancy. | The plug-in software analyzes<br>non-contrast T2 weighted (T2W)<br>and diffusion weighted image<br>(DWI) series to segment the<br>prostate gland and to provide an<br>automatic detection and<br>segmentation of regions<br>suspicious for cancer. For each<br>suspicious region detected, the<br>algorithm moreover provides a<br>lesion Score, by way of PI-<br>RADS interpretation suggestion. |
| likelihood that cancer is present<br>on the mammogram. Patient<br>management decisions should<br>not be made solely on the basis<br>of analysis by Transpara™™. | Outputs of the device include the<br>volume of the prostate and<br>locations, as well as the extent of<br>suspect lesions, with index<br>scores indicating the likelihood<br>that cancer is present, as well as<br>an exam score by way of PI-<br>RADS interpretation suggestion.<br>"Extent of suspect lesions" refers<br>to both the assessment of the<br>boundary of a particular<br>abnormality, as well as<br>identification of multiple<br>abnormalities. In cases where<br>multiple abnormalities are<br>present, ProstatID can be used to<br>assess each abnormality<br>independently. | Outputs of the device should be<br>interpreted consistently with<br>ACR recommendations using all<br>available MR data (e.g.,<br>dynamic contrast-enhanced<br>images [if available]). |
| | Outputs of this device should be<br>interpreted with all available MR<br>data consistent with ACR<br>clinical recommendations (e.g.,<br>dynamic contract enhanced<br>images if available) in context of<br>PI-RADs v2, and in conjunction<br>with bi-parametric MRI acquired<br>with either surface or endorectal<br>MRI accessory coils from<br>compatible MRI systems. | Patient management decisions<br>should not be made solely based<br>on analysis by the Prostate MR<br>AI algorithm. |
### Indications for Use Comparison
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| intended as a replacement for |
|-----------------------------------|
| interpreting prostate |
| abnormalities using MR image |
| data consistent with clinical |
| recommendations (including |
| DCE); nor should patient |
| management decisions be made |
| solely on the basis of ProstatID. |
The indication for Use of Prostate MR AI is similar to that of the predicate device. Both devices are designed for use by medical professionals who analyze radiological images, assisting them in pinpointing and characterizing abnormalities. The devices are both intended to be used concurrently with image interpretation but are not meant to replace a clinician's evaluation or clinical judgement. Thus, the subject and predicate devices are both intended to perform the same type of function and serve the same fundamental role in medical practice.
There are distinctions in the disease-specific abnormalities these devices can identify, the types of medical images they can process, and the specific patient populations they are intended for. The core functionalities of lesion identification and interpretation for medical images remain consistent across the differences. The new concerns regarding the safety and effectiveness of the device raised by these distinctions are assessed and resolved in the device designs to ensure the substantial equivalence.
### Indications for Use/Intended Use Comparison Summary and Conclusion
The Indications for Use were assessed in accordance with the following FDA Guidance Documents:
- The 510(k) Program: Evaluating Substantial Equivalence in Premarket Notifications [510(k)] .
The results of this evaluation determined that the Indications for Use for the subject device and the predicate device are fundamentally equivalent, and only include differences in modality type, body region, and vendors. As such, Siemens Healthineers is of the opinion that the Intended Use and Indications for Use are similar to the predicate device.
# THE PREDICATE DEVICES
21CFR § 807.92(a)(6)
| Attribute | Predicate Device<br>Transpara™<br>K181704 | Reference Device<br>ProstatID™<br>K212783 | Subject Device<br>Prostate MR AI (VA10A) | Equivalency<br>Analysis |
|-------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| General Information | | | | |
| Regulation<br>number | § 892.2090 Radiological<br>Computer Assisted Detection<br>and Diagnosis Software | § 892.2090 Radiological<br>Computer Assisted Detection and<br>Diagnosis Software | § 892.2090 Radiological<br>Computer Assisted Detection<br>and Diagnosis Software | Identical |
| Classification | Class II | Class II | Class II | Identical |
| Product Code | QDQ | QDQ | QDQ | Identical |
| Clinical Characteristics | | | | |
| Attribute | Predicate Device<br>Transpara™<br>K181704 | Reference Device<br>ProstatIDTM<br>K212783 | Subject Device<br>Prostate MR AI (VA10A) | Equivalency<br>Analysis |
| Intended Use<br>(short) | A concurrent reading aid for<br>physicians interpreting<br>screening FFDM acquired with<br>compatible mammography<br>systems, to identify findings and<br>assess their level of suspicion. | A concurrent reading aid for<br>physicians interpreting prostate<br>MRI exams of patients presented<br>for high-risk screening or<br>diagnostic imaging, to identify<br>regions suspicious for prostate<br>cancer and assess their likelihood<br>of malignancy. | A concurrent reading aid for<br>physicians interpreting prostate<br>MRI examinations acquired<br>according to the PI-RADS<br>standard, to identify findings and<br>assess their level of suspicion. | Equivalent -<br>Justified in<br>Section V. |
| Intended<br>patient<br>population | Women undergoing screening<br>mammography | Population of biological males<br>with a prostate gland undergoing<br>screening or clinical MRI exams.<br>This includes biological males of<br>all ages with clinical indicators<br>suggestive of possible prostate<br>cancer or with family history of<br>prostate cancer. | Adult men (40 years and older)<br>with suspected prostate cancer<br>undergoing prostate MRI<br>without prior treatment of the<br>prostate gland (treatment-naïve). | Equivalent -<br>Images<br>captured from<br>different<br>patient<br>populations<br>are<br>standardized<br>to be<br>processible by<br>CAD devices. |
| Anatomical<br>region of<br>interest | Breast | Prostate gland | Prostate gland | Equivalent -<br>Images<br>captured from<br>different<br>anatomical<br>regions are<br>standardized<br>to be<br>processible by<br>CAD devices. |
| Intended<br>Users | physicians qualified to read<br>screening mammograms | Physicians qualified to read and<br>interpret prostate MRI exams<br>consistent with ACR<br>recommendations in the context<br>of PI-RADS v2 | Radiologists qualified to read<br>prostate MRI | Equivalent -<br>- Users are<br>qualified to<br>read radiology<br>images using<br>CAD devices |
| Mode of<br>action | Software that applies algorithms<br>for recognition of suspicious<br>calcifications and soft tissue<br>lesions to detect and<br>characterize findings in<br>radiological breast images and<br>provide information about the<br>presence, location, and<br>characteristics of the findings to<br>the user. | Software that applies algorithms<br>for recognition of suspicious<br>tissue regions in Prostate MR<br>images to provide information<br>about the presence, location, and<br>level of suspicion of the findings. | Software that applies algorithms<br>for recognition of suspicious<br>tissue regions in Prostate MR<br>images to provide information<br>about the presence, location, and<br>level of suspicion of the<br>findings. | Equivalent -<br>Both predicate<br>and subject<br>devices use<br>algorithms to<br>detect findings<br>and provide<br>diagnosis. |
| Method of<br>Use | Concurrent | Concurrent | Concurrent | Identical |
| Attribute | Predicate Device<br>TransparaTM<br>K181704 | Reference Device<br>ProstatIDTM<br>K212783 | Subject Device<br>Prostate MR AI (VA10A) | Equivalency<br>Analysis |
| Visualization<br>Features | Computer aided detection<br>(CAD) marks to highlight<br>locations where the device<br>detected suspicious<br>calcifications or soft tissue<br>lesions. Decision support is<br>provided by region scores on a<br>scale ranging from 0-100, with<br>higher scores indicating a higher<br>level of suspicion. | ProstatID does not include a<br>standalone graphical user<br>interface. Rather, ProstatID<br>outputs are in DICOM format and<br>may be viewed on DICOM-<br>compliant image viewers. | Prostate MR AI does not include<br>a standalone graphical user<br>interface. Rather, it is a plug-in<br>device that is intended to be used<br>with a separate hosting<br>application that allows the user<br>to view the original case, as well<br>as confirm, reject, edit, or add<br>lesions, their contours and<br>Scores. | Equivalent -<br>The difference<br>between<br>predicate and<br>subject<br>devices is<br>justified by<br>using the<br>reference<br>device which<br>also does not<br>have UI while<br>it retains the<br>same level of<br>safety and<br>effectiveness. |
| Technical Characteristics | | | | |
| Design | Software only device | Software only device | Software only device | Identical |
| Automatic<br>Segmentation | Yes | Yes | Yes | Identical |
| Algorithm | Artificial intelligence algorithm<br>trained with large datasets of<br>biopsy proven examples of<br>breast cancer, benign lesions<br>and normal tissue. | Neural network trained on a<br>database of reference normal<br>tissues and abnormalities with<br>known ground truth. | Artificial intelligence algorithm<br>trained on a database of prostate<br>MR image series acquired<br>according to the PI-RADS<br>standard (non-contrast T2W and<br>DWI image series), and<br>corresponding radiological<br>and/or biopsy findings. | Identical - all<br>trained AI<br>algorithms |
| Alteration<br>original<br>image | No | No | No | Identical |
| Data<br>acquisition<br>protocol | Screening mammograms | Prostate MRI image series | Prostate MRI image series<br>acquired according to the PI-<br>RADS standard | Equivalent -<br>Acquired<br>images are<br>qualified for<br>CAD device<br>processing |
| Input | Medical images provided in a<br>DICOM format | Medical images provided in a<br>DICOM format | Medical images provided in a<br>DICOM format | Equivalent -<br>Devices all<br>use<br>standardized |
| Attribute | Predicate Device<br>Transpara™<br>K181704 | Reference Device<br>ProstatID™<br>K212783 | Subject Device<br>Prostate MR AI (VA10A) | Equivalency<br>Analysis |
| Output | • Marks placed on suspicious<br>soft tissue lesions and<br>suspicious calcifications<br>• Region-based scores<br>indicating the likelihood that<br>cancer is present<br>• Overall score indicating the<br>likelihoood that cancer is<br>present on the mammogram | • Marks locations suspicious<br>of lesions<br>• Provides region scores with<br>higher scores indicating a<br>higher level of suspicion<br>• Provides single exam score<br>that synthesizes features | • Automatically segments the<br>contours of the prostate gland<br>• Automatically segments the<br>parts of the prostate that belong<br>to the periheral zone (PZ) and<br>that do not belong to the<br>peripheral zone (non-PZ),<br>respectively<br>• Calculation of a "Suspicion<br>Map" that indicates lesions<br>suspicious for cancer<br>• For each detected lesion:<br>o Lesion contours<br>o Rating of severity ("Score")<br>on a scale from 3 to 5 (in<br>steps of 1). Score is<br>generated by an algorithm<br>trained on the correlation of<br>prostate MRI with PI-RADS<br>scores provided by<br>radiologists and results of<br>lesion-targeted biopsy.<br>• A "Level of Suspicion"<br>(LoS) on a scale from 60 to<br>100 (in steps of 1) as a fine<br>granular measure of the<br>algorithm's suspicion for the<br>presence of a significant<br>lesion, based on training on<br>PI-RADS scores provided by<br>radiologists and results of<br>lesion-targeted biopsy | Equivalent -<br>as far as the<br>different<br>body regions<br>allow. PZ vs.<br>non-PZ is<br>prostate<br>specific and<br>is relevant for<br>PI-RADS<br>evaluation.<br>Instead of<br>marks for<br>suspicious<br>locations in<br>the image, the<br>subject<br>device<br>provides<br>lesion<br>contours and<br>a "suspicion<br>map". |
| Attribute | Predicate Device<br>Transpara™<br>K181704 | Reference Device<br>ProstatID™<br>K212783 | Subject Device<br>Prostate MR AI (VA10A) | Equivalency<br>Analysis |
| Score | Finding level:<br>Continuous score 1-100<br>indicating the level of suspicion<br>of malignancy (from low<br>suspicion to high suspicion).<br>Breast level:<br>None<br>Exam level:<br>10-point scale score indicative<br>of higher frequency of cancer<br>positive | Finding level:<br>Scores on a continuous scale<br>from 0 to 1 that accompany the<br>overlay markings of suspicious<br>locations<br>Case level:<br>Suggested level of suspicion<br>(LoS) or overall PI-RADS exam<br>score | Finding level:<br>Rating of severity ("Score") on a<br>scale from 3 to 5 (in steps of 1).<br>The Score is generated by an<br>algorithm trained on the<br>correlation of pro…
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