Artificial Intelligence‐Supported Video Analysis as a Means to Assess the Impact of DROP‐IN Image Guidance on Robotic Surgeons: Radioguided Sentinel Lymph Node versus PSMA‐Targeted Prostate Cancer Surgery

Abstract

The introduction of the tethered DROP‐IN gamma probe has enabled targeted robot‐assisted radioguided prostate cancer (PCa) resection of pelvic sentinel lymph nodes (SLNs) and prostate‐specific membrane antigen (PSMA)‐positive lesions. While both procedures use 99mTc‐isotopes, the two vary in signal and background intensity. To understand how the different levels of image guidance impact surgical decision‐making, computer‐vision algorithms are used to extract the DROP‐IN probe kinematic form clinical videos. 44 PCa patients undergo SLN (25) and PSMA‐targeted (19) resections. PSMA‐PET/CT and SPECT/CT create preoperative roadmaps, and intraoperative probe signal intensities are recorded. Using neural network‐based software, probe trajectories are extracted from videos to extract multiparametric kinematics and generate decision‐making and dexterity scores. PSMA‐targeted resections yield significantly lower nodal signal intensities in preoperative SPECT‐CT scans (three‐fold; p = 0.01), intraoperative probe readouts (eight‐fold; p < 0.001), and signal‐to‐background ratios (SBR; two‐fold; p < 0.001). Kinematics assessment reveal that the challenges encounter during PSMA‐targeted procedures converted to longer target identification times and increase in probe pick‐ups (both five‐fold; p < 0.001). This results in a fourfold reduction in the decision‐making score (p < 0.001). Reduced signal intensities and intraoperative SBR values negatively affect the impact that image‐guided surgery strategies have on the surgical decision‐making process.