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The Avenda Health AI Prostate Cancer Planning Software is an artificial intelligence (AI)-based decision support software, indicated as an adjunct to the review of magnetic resonance (MR) prostate images and biopsy findings in the prostate oncological workflow. The Avenda Health AI Prostate Cancer Planning Software is designed to support the prostate oncological workflow by helping the user with the segmentation of MR image features, including the prostate; in the evaluation, quantification, and documentation of lesions; and in pre-planning for diagnostic and interventional procedures such as biopsy and/or soft tissue ablation. The device is intended to be used by physicians trained in the oncological workflow in a clinical setting for planning and guidance for clinical, interventional, diagnostic, and/or treatment procedures of the prostate.

The Avenda Health Al Prostate Cancer Planning Software's lesion characterization functions are intended for use on patients with a pathology-confirmed Gleason Grade Group (GGG) ≥ 2 lesion and for whom corresponding biopsy coordinate information have been uploaded. These functions are indicated for the extent of known disease. Extent of known disease refers to the boundary of a pathology confirmed lesion of GGG ≥ 2 for a particular patient. Specifically, using prostate MR images, biopsy, pathology, and clinical data, the device creates and displays a cancer map that assigns a probability to each voxel within the prostate, indicating its probability for containing clinically significant prostate cancer (csPCa, defined as GGG ≥ 2 ). A user selects a threshold for the cancer map to create a boundary of the lesion. The lesion boundary is assigned an Encapsulation Confidence Score indicating the confidence that all csPCa is encapsulated within the boundary. The Encapsulation Confidence Score is from a lookup table generated by a database of cases with known ground-truth. When interpreted by a trained physician, this information may be useful in supporting lesion characterization and subsequent patient management.

The Avenda Health Al Prostate Planning Software may also be used as a medical image application, for the viewing. manipulation, 3D-visualization, and comparison of MR prostate images can be viewed in a number of output formats including volume rendering. It enables visualization of information that would otherwise have to be visually compared disjointedly.

The Avenda Health AI Prostate Cancer Planning Software ("AI Prostate Cancer Planning Software" or "Software") is an artificial intelligence (AI)-based decision support software, indicated as an adjunct to the review of magnetic resonance (MR) prostate images and biopsy findings in the prostate oncological workflow. The Avenda Health AI Prostate Cancer Planning Software is designed to support the prostate oncological workflow by helping the user with the segmentation of MR image features, including the prostate; in the evaluation, quantification, and documentation of lesions; and in pre-planning for diagnostic and interventional procedures such as biopsy and/or soft tissue ablation. The device is intended to be used by physicians trained in the oncological workflow in a clinical setting for planning and guidance for clinical, interventional, diagnostic, and/or treatment procedures of the prostate. The software has three main features:

• 1. Artificial Intelligence (AI) Powered Prostate MRI Segmentation Tool,
• 2. AI Powered Lesion Contour Tool, and
• 3. Simulated Interventional Tool Placement.

The user can choose which subset of features of the Software to employ based on the specific oncological workflow. Not all features are required to be used for every workflow. Once the user has completed planning and has reviewed and verified the information, it can be exported into a supported file format such that it can be imported into a compatible interventional system or biopsy system.

The provided text is a 510(k) summary for the Avenda Health AI Prostate Cancer Planning Software. While it describes the device, its intended use, and generally states that performance testing was conducted, it does not provide a detailed table of acceptance criteria and reported device performance metrics with specific values beyond high-level summaries of reader study results. Similarly, it does not explicitly detail the sample size for the training set or the exact method used to establish ground truth for the training set.

However, based on the provided text, I can extract and infer some information to answer your questions as best as possible.

Acceptance Criteria and Study to Prove Device Meets Criteria

The document states that the device was deemed "substantially equivalent" to a predicate device (Quantitative Insights, Inc. QuantX) based on "performance bench, usability, and reader performance testing." While specific numerical acceptance criteria for each test are not explicitly detailed in a table, the effectiveness of the device is primarily demonstrated through the Multi-Reader, Multi-Case (MRMC) study for human reader performance and standalone performance testing for the prostate segmentation and lesion contouring algorithms.

1. Table of Acceptance Criteria and Reported Device Performance

As a detailed table of specific acceptance criteria values is not present, I will construct a table based on the stated performance outcomes for the key functionalities assessed. The document describes the "superiority" of the device-assisted contours over standard of care, implying these performance improvements were the implicit "acceptance criteria" for demonstrating effectiveness.

Note: The document presents the results as demonstrations of improvement and validation rather than explicitly defined "acceptance criteria" with thresholds that were individually met. The P-values (2 lesions in the intended use population within a representative, independent whole mount pathology dataset of N=50 patients."

7. The Type of Ground Truth Used

• Human Reader Study: "Whole mount pathology data" registered to pre-operative T2-weighted MRI. This is a definitive, pathology-based ground truth.
• Standalone Performance Testing:
  • Prostate Segmentation: "Clinically valid ground truths." (Specifics not provided)
  • Lesion Contouring: "Whole mount pathology data."

8. The Sample Size for the Training Set

The sample size for the training set used to develop the AI algorithms is not explicitly stated in the provided 510(k) summary.

9. How the Ground Truth for the Training Set Was Established

The method for establishing ground truth for the training set is not explicitly stated. The document mentions that the lesion characterization functions' "Encapsulation Confidence Score is from a lookup table generated by a database of cases with known ground-truth." This implies the training data for the lesion characterization component also relied on "known ground-truth," likely pathology, but the process of its establishment is not detailed for the training set itself.

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The Avenda Health Treatment System is indicated for use in surgical applications requiring the ablation, vaporization, excision, incision, and coagulation of soft tissue in areas of surgery including: cardiovascular thoracic surgery (excluding the heart and the vessels in the pericardial sac), dermatology, ear-nose-throat surgery, gastroenterology, general surgery, gynecology, head and neck surgery, neurosurgery, orthopedics, pulmonology, radiology, adiology, and urology, at a wavelength of 980nm.

The Avenda Health Treatment System ("Treatment System") is a thermal laser ablation system that causes coagulation necrosis of soft tissue. The Treatment System consists of the capital system Workstation and the single use Laser Applicator Kit. The Workstation contains core system hardware and software, provides the System's touchscreen user interface (UI), and serves as a "hub" to facilitate the connectivity of other System components. The Laser Applicator Kit consists of two patient-contacting disposable components, the Laser Catheter, and the Thermal-Optical Probe ("TOP"), which facilitate delivery of laser energy and monitoring of temperature, respectively. The Treatment Monitoring Software, which operates on the Workstation, provides the UI for controlling the device, displays previously generated patient and treatment plan information for the procedure, and actively monitors the treatment progress during a procedure by displaying output from the TOP.

The Treatment System is additionally used with an off-the-shelf accessory, the Tubing Set, which transports saline in a closed-loop system between the Workstation and Laser Catheter to provide cooling of the Laser Catheter. The Treatment System further includes an optional patient-contacting disposable accessory, the Multi-Channel Needle Guide ("MCG"), which may be used to attach an ultrasound probe to the Laser Catheter and TOP for enhanced visualization tracking, if desired.

The provided text describes a 510(k) premarket notification for the Avenda Health Treatment System, a laser ablation system for soft tissue coagulation. It focuses on demonstrating substantial equivalence to predicate devices rather than proving a new device meets specific acceptance criteria through clinical studies. Therefore, much of the requested information regarding "acceptance criteria" and "study that proves the device meets acceptance criteria" in the context of an AI/Machine Learning (ML) device is not directly applicable or available in this document.

The document states: "[807.92(b)(2)] Clinical Testing Summary: No clinical testing was conducted to support this 510(k) Premarket Notification." This means there was no human-in-the-loop study (MRMC) or standalone algorithm performance study performed for this submission to evaluate its effectiveness in a traditional clinical setting against a ground truth.

Instead, the submission relies on non-clinical bench testing to demonstrate that the device is as safe and effective as its predicate devices, focusing on the system's physical and technical performance.

Here's a breakdown of the requested information, highlighting what is available and what is not:

1. Table of acceptance criteria and the reported device performance:

Since no clinical efficacy acceptance criteria for an AI/ML component are presented, the "acceptance criteria" here refers to the performance of the physical device as demonstrated through non-clinical testing.

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

• Test set sample size: Not applicable/not specified for clinical testing, as no clinical testing was performed. For non-clinical bench testing, the sample sizes would refer to the number of physical devices or components tested, but these details are not provided in the summary.
• Data provenance: Not applicable for clinical data. For non-clinical testing, the data would originate from the manufacturer's internal testing facilities.

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

Not applicable, as no clinical ground truth was established due to the absence of clinical testing.

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

Not applicable, as no clinical testing requiring expert adjudication was performed.

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:

No, an MRMC comparative effectiveness study was not done. The document explicitly states, "No clinical testing was conducted to support this 510(k) Premarket Notification." The device is a laser ablation system, not an AI/ML diagnostic or assistive tool for human readers.

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

No, a standalone performance study was not done in the context of an AI/ML algorithm. The device is a physical laser system, and its performance was evaluated through non-clinical bench testing.

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

No clinical ground truth (expert consensus, pathology, outcomes data) was used or established, as no clinical testing was conducted. The "ground truth" for this submission are the established engineering and safety standards for medical devices and the performance characteristics of the predicate devices, against which the proposed device's physical and electrical performance was compared.

8. The sample size for the training set:

Not applicable, as there is no mention of an AI/ML component or a "training set" for such a component in the context of this 510(k) submission.

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

Not applicable, as there is no mention of an AI/ML component or a "training set" in the context of this 510(k) submission.

In summary, the provided document outlines a 510(k) submission for a physical medical device (a laser ablation system) that achieved clearance based on demonstrating substantial equivalence through non-clinical bench testing. It does not involve AI/ML components, clinical trials, or the establishment of ground truth for diagnostic or assistive accuracy.

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