Capturing fine-grained details for video-based automation of suturing skills assessment.

OBJECTIVES: Manually-collected suturing technical skill scores are strong predictors of continence recovery after robotic radical prostatectomy. Herein, we automate suturing technical skill scoring through computer vision (CV) methods as a scalable method to provide feedback. METHODS: Twenty-two surgeons completed a suturing exercise three times on the Mimic™ Flex VR simulator. Instrument kinematic data (XYZ coordinates of each instrument and pose) were captured at 30 Hz. After standardized training, three human raters manually video segmented suturing task into four sub-stitch phases (Needle handling, Needle targeting, Needle driving, Needle withdrawal) and labeled the corresponding technical skill domains (Needle positioning, Needle entry, Needle driving, and Needle withdrawal). The CV framework extracted RGB features and optical flow frames using a pre-trained AlexNet. Additional CV strategies including auxiliary supervision (using kinematic data during training only) and attention mechanisms were implemented to improve performance. RESULTS: This study included data from 15 expert surgeons (median caseload 300 [IQR 165-750]) and 7 training surgeons (0 [IQR 0-8]). In all, 226 virtual sutures were captured. Automated assessments for Needle positioning performed best with the simplest approach (1 s video; AUC 0.749). Remaining skill domains exhibited improvements with the implementation of auxiliary supervision and attention mechanisms when deployed separately (AUC 0.604-0.794). All techniques combined produced the best performance, particularly for Needle driving and Needle withdrawal (AUC 0.959 and 0.879, respectively). CONCLUSIONS: This study demonstrated the best performance of automated suturing technical skills assessment to date using advanced CV techniques. Future work will determine if a "human in the loop" is necessary to verify surgeon evaluations.

Energetic-assisted scanning thermal lithography for patterning silver nanoparticles in polymer films.

Energetic-assisted scanning thermal lithography (SThL) was demonstrated with the addition of benzoyl peroxide (BPO) for patterning silver nanoparticles. SThL samples were prepared by spin-coating poly(methyl methacrylate) (PMMA) thin films preloaded with BPO and silver nitrate precursors. Localized thermal analysis via probe heating demonstrated that the BPO decomposition in the polymer film took place at the temperature of 80 °C. Above this temperature, the thermal probe initiated the decomposition of the peroxide, which resulted in the in situ discharge of exothermal energy to compensate the joule shortage and the rapid cooling in the SThL thin film samples. The additional joule energy thermally enhanced the synthesis of silver nanoparticles, which were patterned and embedded in the PMMA thin film. Surface plasmon resonance scattering of these silver nanoparticles was observed by dark-field optical microscopy, whereas the nanoparticle distribution was examined by transmission electron microscopy. Variations in the scanning probe temperatures and peroxide concentrations were carefully investigated to optimize the thermal lithography efficiency upon the addition of energetics.