A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP).

INTRODUCTION: Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, education, and formative assessment. New, sophisticated platforms allow pre-determined segments chosen by surgeons to be automatically presented without the need to review entire videos. This study aimed to validate and demonstrate the accuracy of the first reported AI-based computer vision algorithm that automatically recognizes surgical steps in videos of totally extraperitoneal (TEP) inguinal hernia repair. METHODS: Videos of TEP procedures were manually labeled by a team of annotators trained to identify and label surgical workflow according to six major steps. For bilateral hernias, an additional change of focus step was also included. The videos were then used to train a computer vision AI algorithm. Performance accuracy was assessed in comparison to the manual annotations. RESULTS: A total of 619 full-length TEP videos were analyzed: 371 were used to train the model, 93 for internal validation, and the remaining 155 as a test set to evaluate algorithm accuracy. The overall accuracy for the complete procedure was 88.8%. Per-step accuracy reached the highest value for the hernia sac reduction step (94.3%) and the lowest for the preperitoneal dissection step (72.2%). CONCLUSIONS: These results indicate that the novel AI model was able to provide fully automated video analysis with a high accuracy level. High-accuracy models leveraging AI to enable automation of surgical video analysis allow us to identify and monitor surgical performance, providing mathematical metrics that can be stored, evaluated, and compared. As such, the proposed model is capable of enabling data-driven insights to improve surgical quality and demonstrate best practices in TEP procedures.

The therapeutic potential of tuftsin-phosphorylcholine in giant cell arteritis.

Tuftsin-PhosphorylCholine (TPC) is a novel bi-specific molecule which links tuftsin and phosphorylcholine. TPC has shown immunomodulatory activities in experimental mouse models of autoimmune diseases. We studied herein the effects of TPC ex vivo on both peripheral blood mononuclear cells (PBMCs) and temporal artery biopsies (TABs) obtained from patients with giant cell arteritis (GCA) and age-matched disease controls. GCA is an immune-mediated disease affecting large vessels. Levels of 18 cytokines in supernatants, PBMC viability, T helper (Th) cell differentiation of PBMCs and gene expression in TABs were analyzed. Treatment ex vivo with TPC decreased the production of IL-1ß, IL-2, IL-5, IL-6, IL-9, IL-12(p70), IL-13, IL-17A, IL-18, IL-21, IL-22, IL-23, IFNγ, TNFα, GM-CSF by CD3/CD28 activated PBMCs whereas it negligibly affected cell viability. It reduced Th1 and Th17 differentiation while did not impact Th22 differentiation in PBMCs stimulated by phorbol 12-myristate 13-acetate plus ionomycin. In inflamed TABs, treatment with TPC down-regulated the production of IL-1ß, IL-6, IL-13, IL-17A and CD68 gene expression. The effects of TPC were comparable to the effects of dexamethasone, included as the standard of care, with the exception of a greater reduction of IL-2, IL-18, IFNγ in CD3/CD28 activated PBMCs and CD68 gene in inflamed TABs. In conclusion our results warrant further investigations regarding TPC as an immunotherapeutic agent in GCA and potentially other autoimmune and inflammatory diseases.