Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm.

Objective: The objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)-generated cancer maps versus MRI and conventional nomograms.

Materials And Methods: We retrospectively analysed data from 147 patients who received MRI-targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI-based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk.

Artificial Intelligence Improves the Ability of Physicians to Identify Prostate Cancer Extent.

Purpose: Defining prostate cancer contours is a complex task, undermining the efficacy of interventions such as focal therapy. A multireader multicase study compared physicians' performance using artificial intelligence (AI) vs standard-of-care methods for tumor delineation.

Materials And Methods: Cases were interpreted by 7 urologists and 3 radiologists from 5 institutions with 2 to 23 years of experience.

Prediction and Mapping of Intraprostatic Tumor Extent with Artificial Intelligence.

Background: Magnetic resonance imaging (MRI) underestimation of prostate cancer extent complicates the definition of focal treatment margins.

Objective: To validate focal treatment margins produced by an artificial intelligence (AI) model.

Design Setting And Participants: Testing was conducted retrospectively in an independent dataset of 50 consecutive patients who had radical prostatectomy for intermediate-risk cancer.

Photoacoustic-based visual servoing of a needle tip.

In intraoperative settings, the presence of acoustic clutter and reflection artifacts from metallic surgical tools often reduces the effectiveness of ultrasound imaging and complicates the localization of surgical tool tips. We propose an alternative approach for tool tracking and navigation in these challenging acoustic environments by augmenting ultrasound systems with a light source (to perform photoacoustic imaging) and a robot (to autonomously and robustly follow a surgical tool regardless of the tissue medium). The robotically controlled ultrasound probe continuously visualizes the location of the tool tip by segmenting and tracking photoacoustic signals generated from an optical fiber inside the tool.

Photoacoustic imaging of a human vertebra: implications for guiding spinal fusion surgeries.

It is well known that there are structural differences between cortical and cancellous bone. However, spinal surgeons currently have no reliable method to non-invasively determine these differences in real-time when choosing the optimal starting point and trajectory to insert pedicle screws and avoid surgical complications associated with breached or weakened bone. This paper explores 3D photoacoustic imaging of a human vertebra to noninvasively differentiate cortical from cancellous bone for this surgical task.

Feasibility of photoacoustic-guided teleoperated hysterectomies.

Hysterectomies (i.e., surgical removal of the uterus) are the prevailing solution to treat medical conditions such as uterine cancer, endometriosis, and uterine prolapse.