SFH · Pathology Software Algorithm Device Analyzing Digital Images For Cancer Prognosis

Pathology · 21 CFR 864.3755 · Class 2

Overview

Identification

ArteraAI Prostate is a software only device intended to analyze scanned histopathology whole slide images (WSIs) from treatment-naïve prostate core needle biopsies prepared from formalin fixed paraffin-embedded (FFPE) tissue and stained using Hematoxylin & Eosin (H&E) stains. It provides 10-year risks of distant metastasis and prostate cancer specific mortality and is intended to assist physicians with prognostic risk-based decisions along with other clinicopathological factors in non-metastatic prostate cancer patients (males 55 years of age or older).

Classification Rationale

FDA has determined that the device can be classified in Class II with the establishment of special controls for Class II, which provide reasonable assurance of the safety and effectiveness of the device type.

Special Controls

In combination with the general controls of the FD&C Act, the software algorithm device analyzing digital images for cancer prognosis is subject to the following special controls: (1) The labeling must include the following: (i) Information on the device input(s) (e.g., scanned whole slide images, scanners); (ii) Information about the specimen type (e.g., Hematoxylin & Eosin-stained core needle biopsy, cytology specimen); (iii) Information on the device output(s) and information necessary to interpret the outputs (e.g., risk estimates for recurrence); (iv) A description of intended users and any recommended training necessary for safe use of the device; (v) Limiting statements that indicate: (A) That users should use the device in conjunction with a complete standard of care evaluation; (B) A description of 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), including any limitations in the dataset used to train, test, and tune the algorithm during device development (if applicable); and (C) That the data acquired using the device should only be interpreted by the types of users indicated in the intended use statement. (2) Design verification and validation must include: (i) A description of any datasets used to train, tune, or test the algorithm. The training dataset must include cases representing different variables representative of the conditions likely to be encountered when used as intended (e.g., multiple sites, patient demographics, clinical parameters, challenging diagnostic cases, internal signals and cutoffs); (ii) Device performance data demonstrating appropriate precision and reproducibility of the device along with the following information from the studies: the origin of the digital images, operators (if applicable), location of the study site(s), and digital images corresponding to challenging cases; and (iii) Clinical data demonstrating clinical performance of the device for its intended use, using a dataset representative of the intended use population. The clinical validation dataset must be independent of the data used in the development of the device and must document relevant details, including the origin of the study slides and images, location of the study site(s), and any challenging diagnoses. This clinical data must also include sub-group analysis of relevant population groups.

Recent Cleared Devices (1 of 1)

Top Applicants

• Artera, Inc. — 1 clearance