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

[摘要] 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 Tc-99m-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.

[效力级别]  Early Access

[关键词] AUTOMATED PERFORMANCE METRICS;GAMMA-PROBE;TOOL DETECTION;TRACKING;SKILLS