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 Description

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

Acceptance Criteria	Reported Device Performance
Automatic Prostate Segmentation
Median Dice score between AI algorithm results and ground truth masks exceeds 0.9.	The median of the Dice score between the AI algorithm results and the corresponding ground truth masks exceeds the threshold of 0.9.
Median normalized volume difference between algorithm results and ground truth masks is within ±5%.	The median of the normalized volume difference between the algorithm results and the corresponding ground truth masks is within a ±5% range.
AI algorithm results are statistically non-inferior to individual reader variability (5% margin of error, 5% significance level).	The AI algorithm results as compared to any individual reader are statistically non-inferior based on variabilities that existed among the individual readers within the 5% margin of error and 5% significance level.
Prostate Lesion Detection and Classification
Case-level sensitivity of lesion detection ≥ 0.80 for both radiology and pathology ground truth.	The case-level sensitivity of the lesion detection is equal or greater than 0.80 for both radiology and pathology ground truth.
False positive rate per case of lesion detection < 1 false positive per case for radiology ground truth.	The false positive rate per case of the lesion detection is smaller than one false positive per case for radiology ground truth.
Accuracy of PI-RADS classification of radiology ground truth lesions (detected by algorithm) ≥ 0.8.	The accuracy of the PI-RADS classification of radiology ground truth lesions detected by the algorithm is equal or greater than 0.8.
Non-inferior performance in GE vs Siemens and African American vs non-African American cases, and in cases with peripheral zone vs non-peripheral lesions.	The non-inferior performance of the subject device in GE vs Siemens and African American vs non-African American cases, and in cases with peripheral zone vs non-peripheral lesions was demonstrated. (Note: Specific metrics for this non-inferiority are not explicitly stated as distinct numerical criteria but are stated as "met".)
Clinical Performance (Reader Study - Case-level discrimination of Gleason Grade Group ≥ 1)
Statistically significant improvement in AUROC for aided reading vs unaided reading.	Fully Inclusive Analysis: AUROC improved from 0.6758 (unaided) to 0.7010 (aided), difference of 0.0252 (95% C.I. [0.0011, 0.0493]; P=0.040). Maximally Restrictive Analysis: AUROC improved from 0.6579 (unaided) to 0.6948 (aided), difference of 0.0368 (95% C.I. [0.0108, 0.0628]; P=0.006). In both analyses, the improvement was statistically significant and the primary endpoint thus met.
Clinical Performance (Reader Study - Lesion-level reading performance)
Statistically significant improvement in AUwAFROC for aided reading vs unaided reading.	Fully Inclusive Analysis: AUwAFROC improved in aided reading by 0.0350 (95% C.I.:[0.0020, 0.0681], P=0.037). Maximally Restrictive Analysis: AUwAFROC improved in aided vs. unaided reading by 0.302 (95% C.I.: [0.0080,0.0520], P=0.008). In both analyses, the improvement was statistically significant and the secondary endpoint thus met.
Statistically significant improvement in Fleiss' Kappa for interreader agreement in per-case PI-RADS scores for aided reading vs unaided reading.	Fleiss' Kappa improved from 0.283 (unaided) to 0.371 (aided), with a difference of 0.087 (95% C.I. [0.051, 0.125]). The improvement was statistically significant (P<0.0001).

Study Information

2. Sample size used for the test set and the data provenance:

Automatic Prostate Segmentation: 222 transversal T2 series.
- Provenance: More than 10 clinical sites.
- Retrospective/Prospective: Not explicitly stated, but the description of comparing against ground truth generated implies retrospective use of existing scans.
Prostate Lesion Detection and Classification (Standalone Performance):
- 105 cases from 6 sites (against radiology ground truth).
- 115 cases from 6 sites (against pathology ground truth).
- 340 cases from the multi-reader multi-case study (used for evaluation, implied prospective for this part of the evaluation, but the cases themselves were retrospective for the reader study).
- Provenance: 6 sites (for 105 and 115 cases), and two US sites (for 340 cases).
- Retrospective/Prospective: The cases for the lesion detection and classification evaluation were used to compare against established ground truths, suggesting retrospective analysis of existing data. The cases for the reader study were retrospectively selected.
Multi-Reader Multi-Case (MRMC) Study: 340 cases.
- Provenance: Two US sites. Cases were consecutive and specifically included additional consecutive patient cases from men of African descent to ensure at least 13% Black or African American ethnicity.
- Retrospective/Prospective: Cases were selected retrospectively.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

Automatic Prostate Segmentation: 3 expert radiologists. No specific years of experience or subspecialty beyond "radiologists" are mentioned but implied as "expert".
Prostate Lesion Detection and Classification (Radiology Ground Truth): 3 expert radiologists in prostate MRI reading.
MRMC Study (Lesion-level reference standard): 3 experienced radiologists acting as Truthers.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

Automatic Prostate Segmentation: Pixel-wise consensus among the 3 expert radiologists.
Prostate Lesion Detection and Classification (Radiology Ground Truth): Consensus reading of the 3 expert radiologists.
MRMC Study (Case-level reference standard): Biopsy results (Gleason Grade Group GGG ≥ 1), or for cases without biopsy, PSA density and follow-up data.
MRMC Study (Lesion-level reference standard): Consensus lesions with a consensus PI-RADS of at least 3 from majority voting among the 3 experienced radiologists. (This implies a form of consensus/majority vote).

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

Yes, an MRMC study was done with a paired split-plot design, combining two fully-crossed MRMC sub-studies.

Case-level AUROC improvement (discriminating Gleason Score ≥ 1):
- Fully Inclusive Analysis: +0.0252 (from 0.6758 unaided to 0.7010 aided).
- Maximally Restrictive Analysis: +0.0368 (from 0.6579 unaided to 0.6948 aided).
Lesion-level AUwAFROC improvement:
- Fully Inclusive Analysis: +0.0350.
- Maximally Restrictive Analysis: +0.0302.
Fleiss' Kappa (interreader agreement in per-case PI-RADS scores) improvement: +0.087 (from 0.283 unaided to 0.371 aided).

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

Yes, standalone performance was evaluated for:

Automatic Prostate Segmentation: Compared algorithm results to ground truth generated by radiologists.
Prostate Lesion Detection and Classification: Compared automatic detection and classification results to radiology ground truth and pathology ground truth.
MRMC Study (AI Standalone reference): The ROC curves shown graphically include a "grey curve [that] denotes AI standalone performance."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

For Automatic Prostate Segmentation: Pixel-wise consensus from 3 expert radiologists.
For Prostate Lesion Detection and Classification:
- Consensus reading of 3 expert radiologists (radiology ground truth).
- Biopsy results for the same patient (pathology ground truth).
For MRMC Study (Case-level): Biopsy results (Gleason Grade Group GGG ≥ 1), and in cases where biopsy was unavailable, PSA density and follow-up (12 months negative by PSA or MRI).
For MRMC Study (Lesion-level): Consensus lesions with a consensus PI-RADS of at least 3 from majority voting among 3 experienced radiologists.

8. The sample size for the training set:

The document states: "The cases for the reader study were kept completely separate from those used for the training of the Prostate MR AI algorithm." However, it does not specify the sample size for the training set. It only mentions that the AI algorithm was "trained on a database of prostate MR image series acquired according to the PI-RADS standard (non-contrast T2W and DWI image series), and corresponding radiological and/or biopsy findings."

9. How the ground truth for the training set was established:

The ground truth for the training set was established based on "corresponding radiological and/or biopsy findings." Specific details on the adjudication method (e.g., number of experts, consensus process) for the training set are not provided in this document, only the source of the ground truth.

Summary

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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 DeviceTranspara™K181704	Reference DeviceProstatID™K212783	Subject DeviceProstate MR AI (VA10A)
The ScreenPoint Transpara™system is intended for use as aconcurrent reading aid forphysicians interpreting screeningmammograms, to identifyregions suspicious for breastcancer and assess theirlikelihood of malignancy.Output of the device includesmarks placed on suspicious softtissue lesions and suspiciouscalcifications; region-basedscores, displayed upon thephysician's query, indicating the	ProstatID™ is a radiologicalcomputer assisted detection(CADe) and diagnostic (CADx)software device for use in ahealthcare facility or hospital toassist trained radiologists in thedetection, assessment andcharacterization of prostateabnormalities, including cancerlesions using MR image data	Prostate MR AI is a plug-inRadiological Computer AssistedDetection and DiagnosisSoftware device intended to beused• with a separate hostingapplication• as a concurrent reading aidto assist radiologists in theinterpretation of a prostateMRI examination acquired
likelihood that cancer is present	with the following indicationsfor use.	according to the PI-RADSstandard
in specific regions; and an	ProstatID analyzes T2W, DWIand ADC MRI data. ProstatIDdoes not include DCE images inits analysis.	in adult men (40 years andolder) with suspected cancerin treatment naïve prostateglands
overall score indicating the	ProstatID software is intendedfor use as a concurrent readingaid for physicians interpretingprostate MRI exams of patientspresented for high-risk screeningor diagnostic imaging, fromcompatible MRI systems, toidentify regions suspicious forprostate cancer and assess theirlikelihood of malignancy.	The plug-in software analyzesnon-contrast T2 weighted (T2W)and diffusion weighted image(DWI) series to segment theprostate gland and to provide anautomatic detection andsegmentation of regionssuspicious for cancer. For eachsuspicious region detected, thealgorithm moreover provides alesion Score, by way of PI-RADS interpretation suggestion.
likelihood that cancer is presenton the mammogram. Patientmanagement decisions shouldnot be made solely on the basisof analysis by Transpara™™.	Outputs of the device include thevolume of the prostate andlocations, as well as the extent ofsuspect lesions, with indexscores indicating the likelihoodthat cancer is present, as well asan exam score by way of PI-RADS interpretation suggestion."Extent of suspect lesions" refersto both the assessment of theboundary of a particularabnormality, as well asidentification of multipleabnormalities. In cases wheremultiple abnormalities arepresent, ProstatID can be used toassess each abnormalityindependently.	Outputs of the device should beinterpreted consistently withACR recommendations using allavailable MR data (e.g.,dynamic contrast-enhancedimages [if available]).
	Outputs of this device should beinterpreted with all available MRdata consistent with ACRclinical recommendations (e.g.,dynamic contract enhancedimages if available) in context ofPI-RADs v2, and in conjunctionwith bi-parametric MRI acquiredwith either surface or endorectalMRI accessory coils fromcompatible MRI systems.	Patient management decisionsshould not be made solely basedon analysis by the Prostate MRAI 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 DeviceTranspara™K181704	Reference DeviceProstatID™K212783	Subject DeviceProstate MR AI (VA10A)	EquivalencyAnalysis
General Information
Regulationnumber	§ 892.2090 RadiologicalComputer Assisted Detectionand Diagnosis Software	§ 892.2090 RadiologicalComputer Assisted Detection andDiagnosis Software	§ 892.2090 RadiologicalComputer Assisted Detectionand Diagnosis Software	Identical
Classification	Class II	Class II	Class II	Identical
Product Code	QDQ	QDQ	QDQ	Identical
Clinical Characteristics
Attribute	Predicate DeviceTranspara™K181704	Reference DeviceProstatIDTMK212783	Subject DeviceProstate MR AI (VA10A)	EquivalencyAnalysis
Intended Use(short)	A concurrent reading aid forphysicians interpretingscreening FFDM acquired withcompatible mammographysystems, to identify findings andassess their level of suspicion.	A concurrent reading aid forphysicians interpreting prostateMRI exams of patients presentedfor high-risk screening ordiagnostic imaging, to identifyregions suspicious for prostatecancer and assess their likelihoodof malignancy.	A concurrent reading aid forphysicians interpreting prostateMRI examinations acquiredaccording to the PI-RADSstandard, to identify findings andassess their level of suspicion.	Equivalent -Justified inSection V.
Intendedpatientpopulation	Women undergoing screeningmammography	Population of biological maleswith a prostate gland undergoingscreening or clinical MRI exams.This includes biological males ofall ages with clinical indicatorssuggestive of possible prostatecancer or with family history ofprostate cancer.	Adult men (40 years and older)with suspected prostate cancerundergoing prostate MRIwithout prior treatment of theprostate gland (treatment-naïve).	Equivalent -Imagescaptured fromdifferentpatientpopulationsarestandardizedto beprocessible byCAD devices.
Anatomicalregion ofinterest	Breast	Prostate gland	Prostate gland	Equivalent -Imagescaptured fromdifferentanatomicalregions arestandardizedto beprocessible byCAD devices.
IntendedUsers	physicians qualified to readscreening mammograms	Physicians qualified to read andinterpret prostate MRI examsconsistent with ACRrecommendations in the contextof PI-RADS v2	Radiologists qualified to readprostate MRI	Equivalent -- Users arequalified toread radiologyimages usingCAD devices
Mode ofaction	Software that applies algorithmsfor recognition of suspiciouscalcifications and soft tissuelesions to detect andcharacterize findings inradiological breast images andprovide information about thepresence, location, andcharacteristics of the findings tothe user.	Software that applies algorithmsfor recognition of suspicioustissue regions in Prostate MRimages to provide informationabout the presence, location, andlevel of suspicion of the findings.	Software that applies algorithmsfor recognition of suspicioustissue regions in Prostate MRimages to provide informationabout the presence, location, andlevel of suspicion of thefindings.	Equivalent -Both predicateand subjectdevices usealgorithms todetect findingsand providediagnosis.
Method ofUse	Concurrent	Concurrent	Concurrent	Identical
Attribute	Predicate DeviceTransparaTMK181704	Reference DeviceProstatIDTMK212783	Subject DeviceProstate MR AI (VA10A)	EquivalencyAnalysis
VisualizationFeatures	Computer aided detection(CAD) marks to highlightlocations where the devicedetected suspiciouscalcifications or soft tissuelesions. Decision support isprovided by region scores on ascale ranging from 0-100, withhigher scores indicating a higherlevel of suspicion.	ProstatID does not include astandalone graphical userinterface. Rather, ProstatIDoutputs are in DICOM format andmay be viewed on DICOM-compliant image viewers.	Prostate MR AI does not includea standalone graphical userinterface. Rather, it is a plug-indevice that is intended to be usedwith a separate hostingapplication that allows the userto view the original case, as wellas confirm, reject, edit, or addlesions, their contours andScores.	Equivalent -The differencebetweenpredicate andsubjectdevices isjustified byusing thereferencedevice whichalso does nothave UI whileit retains thesame level ofsafety andeffectiveness.
Technical Characteristics
Design	Software only device	Software only device	Software only device	Identical
AutomaticSegmentation	Yes	Yes	Yes	Identical
Algorithm	Artificial intelligence algorithmtrained with large datasets ofbiopsy proven examples ofbreast cancer, benign lesionsand normal tissue.	Neural network trained on adatabase of reference normaltissues and abnormalities withknown ground truth.	Artificial intelligence algorithmtrained on a database of prostateMR image series acquiredaccording to the PI-RADSstandard (non-contrast T2W andDWI image series), andcorresponding radiologicaland/or biopsy findings.	Identical - alltrained AIalgorithms
Alterationoriginalimage	No	No	No	Identical
Dataacquisitionprotocol	Screening mammograms	Prostate MRI image series	Prostate MRI image seriesacquired according to the PI-RADS standard	Equivalent -Acquiredimages arequalified forCAD deviceprocessing
Input	Medical images provided in aDICOM format	Medical images provided in aDICOM format	Medical images provided in aDICOM format	Equivalent -Devices allusestandardized
Attribute	Predicate DeviceTranspara™K181704	Reference DeviceProstatID™K212783	Subject DeviceProstate MR AI (VA10A)	EquivalencyAnalysis
Output	• Marks placed on suspicioussoft tissue lesions andsuspicious calcifications• Region-based scoresindicating the likelihood thatcancer is present• Overall score indicating thelikelihoood that cancer ispresent on the mammogram	• Marks locations suspiciousof lesions• Provides region scores withhigher scores indicating ahigher level of suspicion• Provides single exam scorethat synthesizes features	• Automatically segments thecontours of the prostate gland• Automatically segments theparts of the prostate that belongto the periheral zone (PZ) andthat do not belong to theperipheral zone (non-PZ),respectively• Calculation of a "SuspicionMap" that indicates lesionssuspicious for cancer• For each detected lesion:o Lesion contourso Rating of severity ("Score")on a scale from 3 to 5 (insteps of 1). Score isgenerated by an algorithmtrained on the correlation ofprostate MRI with PI-RADSscores provided byradiologists and results oflesion-targeted biopsy.• A "Level of Suspicion"(LoS) on a scale from 60 to100 (in steps of 1) as a finegranular measure of thealgorithm's suspicion for thepresence of a significantlesion, based on training onPI-RADS scores provided byradiologists and results oflesion-targeted biopsy	Equivalent -as far as thedifferentbody regionsallow. PZ vs.non-PZ isprostatespecific andis relevant forPI-RADSevaluation.Instead ofmarks forsuspiciouslocations inthe image, thesubjectdeviceprovideslesioncontours anda "suspicionmap".
Attribute	Predicate DeviceTranspara™K181704	Reference DeviceProstatID™K212783	Subject DeviceProstate MR AI (VA10A)	EquivalencyAnalysis
Score	Finding level:Continuous score 1-100indicating the level of suspicionof malignancy (from lowsuspicion to high suspicion).Breast level:NoneExam level:10-point scale score indicativeof higher frequency of cancerpositive	Finding level:Scores on a continuous scalefrom 0 to 1 that accompany theoverlay markings of suspiciouslocationsCase level:Suggested level of suspicion(LoS) or overall PI-RADS examscore	Finding level:Rating of severity ("Score") on ascale from 3 to 5 (in steps of 1).The Score is generated by analgorithm trained on thecorrelation of prostate MRI withPI-RADS scores provided byradiologists and results of lesion-targeted biopsyA "Level of Suspicion" (LoS) ona scale from 60 to 100 (in stepsof 1) as a more granular measureof the algorithm's suspicion forthe presence of a significantlesion, based on training on PI-RADS scores provided byradiologists and results of lesion-targeted biopsyProstate level (= Exam Level):LoS derived from Finding Levelresults as maximum LoS over allfindings (on a scale of 60-100 insteps of 1), or (in the absence offindings) as maximum ofinternal Suspicion Map overprostate segmentation (on a scaleof 1-59 in steps of 1)	Equivalent -Thedifferencesbetweenpredicate andsubjectdevices can bejustified byusing thereferencedevice whichapplycontinuousfinding scaleand share thesame PI-RADS LoSexam score.
Findingdiscovery	Findings are by-defaultdisplayed when score is equal orhigher than 5.Upon user request for findingsof score equal or less than 4.	Findings are added to post-processed T2W image in DICOMformat as a colorized translucentoverlay to highlight locations, asoverlay scores on a continuousscale from 0 to 1, and as asuggested LoS or overall PI-RADS exam score.	Upon activation of the plug-in,findings are displayed in thehosting workflow assegmentations on the T2Wimage series with correspondingratings by Scores (value range:3-5), and as an overall examScore.	Equivalent -Findingresults of bothpredicate andsubjectdevices can bedisplayed.
Attribute	Predicate DeviceTranspara™™K181704	Reference DeviceProstatID™K212783	Subject DeviceProstate MR AI (VA10A)	EquivalencyAnalysis
Performance	Reader study with 6720 reads:• fully crossed multi-reader,multi-case design• 240 cases• 14 radiologists	Reader study with 2700 reads:• 150 cases were read by 9trained physicians in twoseparate reads - first withoutProstatID, and second withProstatID.	Reader study with 4080 reads:• split-plot multi-reader, multi-case design(comprising 2 fully crossedmulti-reader, multi-case splits)• 2× 170 = 340 cases from 2consecutively acquired cohorts• 2× 6 = 12 radiologists• Two analysis scenarios:a) all cases, all readersb) only cases with biopsy, or anegative MRI and ≥12months negative follow-upby PSA or MRI (exclusionof 34 cases), and only case-reader pairs in whichreaders were not involvedin prostate MRI reading atthe institution providing thecase during the time of datacollection (exclusion of459 case-reader pairs)	Equivalent -The differencebetween thepredicate andsubjectdevices can bejustified bycomparingwith theperformanceof thereferencedevice. Readerstudies of bothreference andsubjectdevices usedsimilarstatisticallymeaningfulcase numbers,qualifiedreaders,acceptancecriteria andyieldedcomparablestudy results.
	Case-level ROC:• radiologists' AUC unaided =0.866• radiologists' AUC aided =0.886• mean difference: +0.020,95% C.I. [0.010, 0.030]	Case-level ROC for discriminatingGleason Score ≥ 7 in those 130 outof the 150 cases that had biopsyresults:• radiologists' AUC unaided =0.629• radiologists' AUC aided =0.671• mean difference: +0.042,95% C.I. [0.005, 0.080]	Case-level ROC fordiscriminating Gleason Score ≥ 6:• radiologists' AUC (unaided) =a) 0.676; b) 0.658• radiologists' AUC (aided) =a) 0.701; b) 0.695• mean difference:a) +0.025,95% C.I. [0.001, 0.049]b) +0.037,95% C.I. [0.011, 0.063]
		Lesion-level wAFROC:• radiologists' AUC unaided =0.387• radiologists' AUC aided =0.430• mean difference: +0.043,95% C.I. [0.003, 0.083]	Lesion-level wAFROC:• radiologists' AUC unaided =a) 0.734; b) 0.772• radiologists' AUC aided =a) 0.769; b) 0.834• mean difference:a) +0.035,95% C.I. [0.002, 0.068]b) +0.030,95% C.I. [0.008, 0.052]

VI. COMPARISON OF FEATURES AND SPECIFICATIONS WITH

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Healt

Prostate MR AI (VA10A) Traditional 510(k) Submission

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Prostate MR AI (VA10A) Traditional 510(k) Submission

The predicate and subject devices are both Computer Assisted Detection and Diagnosis Software devices to assist radiologists to detect and diagnose diseases based on the radiological images. They share

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significant similarities in the functionalities of detection, assessment and characterization of human tissue abnormalities on radiological images using AI/ML augmented technologies.

While technological characteristics of the predicate device Transpara™, e.g., network architecture and training process, remain unknown to the public, the verification and validation testing of the subject device Prostate MR AI demonstrate that the device can perform prostate gland segmentation, lesion detection and classification as intended, meeting all the design inputs. The risks associated with the algorithm development are mitigated as far as possible. The detection and diagnosis accuracy of the subject device was assessed to validate the appropriateness and implementation of the intended use. In addition, a Multi-Reader/Multi-Case Study was performed to demonstrate that radiological reading aided by Prostate MR AI yields better diagnostic performance than unaided reading. Evidence provided within this submission demonstrates conformance with special controls for software as medical devices. The differences in technological characteristics between the subject device Prostate MR AI and the predicate device do not constitute any new intended use and do not raise new questions of safety and effectiveness.

The other differences between the subject device and the predicate device, notably the body regions targeted, are justified based on the reference device ProstatIDTM.

In summary, Siemens is of the opinion that Prostate MR AI (VA10A) does not raise new or different questions of safety or effectiveness and is substantially equivalent to the currently marketed predicate device Transpara™ (K181704).

VII. PERFORMANCE DATA

The following performance data were provided in support to demonstrate similarities to the predicate / previously cleared device.

Summary of Software Verification and Validation

No performance standards for CADe/CADx have been issued under the authority of Section 514. Nonclinical testing was conducted for the device Prostate MR AI (VA10A) during product development. The features described in this Premarket Notification were supported with verification and validation testing.

Siemens Healthineers claims conformance to the following recognized consensus standards:

ISO 14971 Third Edition 2019-12
IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION
IEC 82304-1 Edition 1.0 2016-10 .

Software documentation for Basic Documentation Level per FDA's Guidance Document "Content of Premarket Submissions for Device Software Functions" issued on June 14, 2023 is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests were conducted on the device Prostate MR AI during product development.

The Risk Analysis was completed, and risk control implemented to mitigate identified hazards. The testing results support that all the software specifications have met the acceptance criteria. Testing for verification and validation for the device was found acceptable to support the claims of substantial equivalence.

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Nonclinical Test Summary

21CFR § 807.92(b)(2)

Automatic prostate segmentation

To monitor the performance of the automatic prostate segmentation, an automated test routine was established that compared the segmentation result of 222 transversal T2 series from more than 10 clinical sites against ground truth generated by radiologists. The image data base of this formal test includes ~31% cases acquired with a 1.5T system and ~69% cases acquired with a 3T system. Of the test data 41%, 27%, and 32% were scanned on Siemens, Philips, and GE MR systems, respectively.

The reference standard was established through pixel-wise consensus, which was built based on annotation results of three expert radiologists. Two metrics were used for the evaluation. One was based on the Dice score of pairs of segmentation masks, and the other was based on the normalized volume difference based on the computed volumes of masks.

The overall results demonstrate the following:

. The median of the Dice score between the AI algorithm results and the corresponding ground truth masks exceeds the threshold of 0.9.
. The median of the normalized volume difference between the algorithm results and the corresponding ground truth masks is within a ±5% range.
. The AI algorithm results as compared to any individual reader are statistically non-inferior based on variabilities that existed among the individual readers within the 5% margin of error and 5% significance level.

Prostate lesion detection and classification

To monitor the performance of the automatic prostate lesion and classification, an automated test routine was established that compared the result of the automatic lesion detection and classification result for

. 105 cases from 6 sites against a ground truth generated by radiologists,
115 cases from 6 sites against a ground truth generated by prostate biopsy, as well as ●
. 340 cases from the multi-reader multi-case study.

The reference standard for the radiology ground truth was established through consensus reading of three expert radiologists in prostate MRI reading. The reference standard for the pathology ground truth was established through biopsy results for the same patient. Therefore, in the pathology ground truth, only case level annotation was available, while in the radiology ground truth, lesion level annotation was also included. Sensitivity and false positive rate per case was used to evaluate the performance of prostate lesion detection and classification, and accuracy was used to determine the performance of the PI-RADS classification.

The overall results demonstrated the following:

. The case level sensitivity of the lesion detection is equal or greater than 0.80 for both radiology and pathology ground truth.
. The false positive rate per case of the lesion detection is smaller than one false positive per case for radiology ground truth.
. The accuracy of the PI-RADS classification of radiology ground truth lesions detected by the algorithm is equal or greater than 0.8.

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The non-inferior performance of the subject device in GE vs Siemens and African American vs ● non-African American cases, and in cases with peripheral zone vs non- peripheral lesions
All pre-specified criteria for non-clinical testing were therefore met.

Clinical Test Summary

21CFR § 807.92(b)(2)

For assessment of the clinical performance of the device, a reader study was conducted with the objective to determine whether individual radiologists perform better with than without Prostate MR AI in the task of identifying cases of treatment-naïve men that are suspicious of prostate cancer based on a prostate MRI examination acquired according to the PI-RADS standard. In order to compare the reading performance of radiologists with and without the aid of Prostate MR AI, a study design with independent arms for aided and unaided reading is appropriate to test both reading conditions. The study was set up as a multi-reader multi-case (MRMC) study in a paired split-plot design, which essentially combined two fully-crossed MRMC (sub-)studies conducted in parallel, each using half of the overall readers and half of the overall cases.

Within each of the two MRMC sub-studies, multiple radiologists performed two reads of multiple prostate MRIs, one without and one with the support of Prostate MR AI. Between the sessions there was a wash-out time interval of at least 28 days. All of the 12 Readers were American Board of Radiology certified and selected to reflect a spectrum of experience and practice type background.

Study cases were selected retrospectively to be representative of the population of U.S. men undergoing prostate MRI without prior treatment of the prostate gland.

For inclusion in the reader study, 340 cases were selected. The cases from two US sites were consecutive and not enriched for positive cases. To adequately represent African American patients, who are more likely to be diagnosed with prostate cancer, present at an earlier age, and are more likely to have advanced disease at diagnosis', additional consecutive patient cases specifically from men of African descent were included to ensure that at least 13% of the study cases are of Black or African American ethnicity2.

The cases for the reader study were kept completely separate from those used for the training of the Prostate MR AI algorithm.

The primary endpoint of the study was based on the comparison of case-level diagnostic performance of aided and unaided reads using the reader-provided case-level LoS (RLoS), that is, a PI-RADS scale with additional intermediate steps of 0.5. The resulting finer granularity, which is not used in clinical practice, was introduced for improved accuracy of the comparative assessment. Biopsy results (with the criterion Gleason Grade Group GGG greater or equal to 1. i.e. any cancer on biopsy), and in case they were not available, PSA density and follow-up were used to determine the reference standard.

However, as consecutive cases were used in order to avoid selection bias, not all cases had unquestionable ground truth in terms of biopsy information or a negative MRI with negative follow-up of at least 12 months by PSA density or MRI. Therefore, two alternative evaluations were performed in

1 Smith ZL, Eggener SE, Murphy AB: African-American Prostate Cancer Disparities. Curr Urol Rep 18, 81 (2017)

2 https://www.census.gov/quickfacts/fact/table/US/IPE120219 (accessed on Feb 6th, 2022) gives an estimate of 13.4% for the U.S. population of Black or African American ethnicity for July 15, 2021

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parallel: a fully inclusive and a maximally restrictive analysis.

In the fully inclusive scenario, all cases and readers were considered, using a probabilistic treatment if definite reference standard labels were not available.

In the maximally restrictive scenario, only cases with biopsy information, or a negative MRI and 12month negative follow-up by PSA density or MRI, were used. Moreover, reader-case pairs were excluded if readers were involved in clinical prostate MRI evaluation at the institution that provided the cases during the period of data collection.

In the fully inclusive analysis, the average area under the ROC (Receiver Operating Characteristic) curve (AUROC) improved from 0.6758 in unaided reading to 0.7010 in aided reading, with a difference of 0.0252 (95% C.I. [0.0011, 0.0493]; P=0.040).

In the maximally restrictive analysis, AUROC improved from 0.6579 in unaided reading to 0.6948 in aided reading, with a difference of 0.0368 (95% C.I. [0.0108, 0.0628]; P=0.006).

In either analysis, the improvement was statistically significant and the primary endpoint thus met.

The following figures show the pooled ROC curves and RLoS ≥ 3 operating points for the discrimination of any cancer on biopsy (Gleason Grade Group ≥ 1) in unaided (orange) and aided reading (blue). The grey curve denotes AI standalone performance (with 95% confidence interval envelope); left: fully inclusive analysis, right: maximally restrictive analysis.

Image /page/16/Figure/9 description: The image contains two identical ROC curves, which plot sensitivity versus 1-specificity. Both curves show a blue line, an orange line, and a gray dotted line. The blue line is generally above the orange line, indicating better performance. There are also gray dots on the plot.

In the fully inclusive analysis, the average sensitivity/specificity of the Readers at a case-level RLOS threshold of ≥ 3 was 0.57 (95% C.I.: [0.49, 0.64]) / 0.72 (95% C.I. [0.64, 0.79]) in unaided and 0.60 (95% C.I .: [0.53, 0.68]) / 0.73 (95% C.I .: [0.65, 0.80]) in aided reading.

For the secondary endpoint (an analysis of the lesion-level reading performance in unaided and aided reading), AUwAFROC (area under the average weighted alternative free receiver operating characteristic) figures of merit were determined, using as reference standard consensus lesions with a consensus PI-RADS of at least 3 from majority voting among 3 experienced radiologists acting as Truthers. Against this, the Readers' corresponding true positives (correct lesion localizations) and false positive lesion detections were held, with their respective ratings.

Although in this analysis the reference standard is not in question for any case, for full equivalence to the primary analysis the evaluation was performed both for the fully inclusive and the maximally restrictive analysis scenario.

In the fully inclusive analysis, AUwAFROC improved in aided reading by 0.0350 (95% C.I.:[0.0020, 0.0681], P=0.037).

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In the maximally restrictive analysis, AUwAFROC improved in aided vs. unaided reading by 0.302 (95% C.I.: [0.0080,0.0520], P=0.008).

In either analysis, the improvement was statistically significant and the secondary endpoint thus met.

In a supplemental analysis for the fully inclusive analysis scenario, Fleiss' Kappa for interreader agreement in per-case PI-RADS scores was 0.283 (95% C.I.: [0.242, 0.322]) for unaided reads, and 0.371 (95% C.I.: [0.326, 0.411]) for aided reads, with a difference of 0.087 (95% C.I. [0.051, 0.125]). The improvement in Fleiss' Kappa between unaided and aided reads was statistically significant (P<0.0001).

The device thus passed clinical testing for case-level and lesion-level reading performance improvement in aided vs. unaided reads.

Summary

Performance tests were conducted to test the functionality of the device Prostate MR AI (VA10A). These tests have been performed to assess the functionality of the subject device. Results of all conducted testing were found acceptable in supporting the claim of substantial equivalence.

Safety and Effectiveness Information

Software design description, hazard analysis, and technical and safety information have been completed and provided in support of this device labeling contains instructions for use with cautions to provide for safe and effective use of the device. The results of the hazard analysis, combined with the appropriate preventive measures taken indicate the device are included in documentations of Basic Documentation Level, as per Guidance "Content of Premarket Submissions for Device Software Functions", June 2023.

The device has no PHI and is utilized only by trained professionals. The output of the device is evaluated by trained professionals as a concurrent reader. Use of this device does not impact the quality or status of the original acquired data.

VII. CONCLUSIONS

21CFR § 807.92(b)(3)

In accordance with Guidance Document "The 510(k) Program: Evaluating Substantial Equivalence in Premarket Notifications [510(k)]', the predicate device for this submission is Transpara™, cleared via Premarket Notification K181704 on November 21, 2018.

On the basis of the comparison of device properties provided above including intended use, technological characteristics, performance and risks, Siemens Healthineers believes that the subject device, Prostate MR AI (VA10A) is as safe and effective as and substantially equivalent to the predicate device, Transpara™.

Regulation Number and Section

§ 892.2090 Radiological computer-assisted detection and diagnosis software.

(a)
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.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis 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.