Artificial Intelligence Assisted Recognition of Anatomical Landmarks and Laparoscopic Instruments in Transabdominal Preperitoneal Inguinal Hernia Repair.

Autor:	Zygomalas A; AI Assisted Laparoscopy Research Program, Hellenic Open University, Patras, Greece.; Department of Minimally Invasive Surgery, Olympion General Clinic of Patras, Patras, Greece., Kalles D; AI Assisted Laparoscopy Research Program, Hellenic Open University, Patras, Greece., Katsiakis N; Department of Minimally Invasive Surgery, Olympion General Clinic of Patras, Patras, Greece., Anastasopoulos A; Department of Minimally Invasive Surgery, Olympion General Clinic of Patras, Patras, Greece., Skroubis G; AI Assisted Laparoscopy Research Program, Hellenic Open University, Patras, Greece.; Department of General Surgery, University Hospital of Patras, Patras, Greece.
Jazyk:	angličtina
Zdroj:	Surgical innovation [Surg Innov] 2024 Apr; Vol. 31 (2), pp. 178-184. Date of Electronic Publication: 2024 Jan 09.
DOI:	10.1177/15533506241226502
Abstrakt:	Laparoscopic TAPP (Trans-Abdominal PrePeritoneal) is a minimally invasive surgical procedure used to repair inguinal hernias. Arguably, one important aspect to TAPP hernia repair is the identification of anatomical landmarks and the correct use of various laparoscopic instruments. There are very few studies regarding the use of artificial intelligence in laparoscopic inguinal hernia repair and more specifically in TAPP. The aim of this study is to evaluate the feasibility and usefulness of AI in the recognition of anatomical landmarks and tools in laparoscopic TAPP videos. Imaging data have been exported from 20 Laparoscopic TAPP videos that have been performed by the authors and another 5 high quality TAPP videos from the internet (free access) performed by other surgeons. In total 1095 selected images have been exported for annotation. To accomplish the AI result of computer vision, the YOLOv8 model of deep learning was used. In total 2716 segmented areas of interest have been exported. The AI model was able to detect the various classes with a maximum F1 score of .82 when the confidence threshold was set to .406. The mAP50 was .873 for all classes. The Precision was above 50% when the confidence was over 10%. The Recall rate was above 50% when confidence was less than 70%. These results suggest that the model is effective at balancing precision and recall, capturing both true positives and minimizing false negatives. Artificial Intelligence recognition of anatomical landmarks and laparoscopic instruments in TAPP is feasible with acceptable success rates. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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