Ido Nachmany1, Niv Pencovich2, Yiftach Barash3, Eyal Klang3, Adar Lux3, Eli Konen3, Nir Horesh1, Ron Pery1, Nadav Zilka1, Rony Eshkenazy1. 1. Department of General Surgery and Transplantation, Sheba Medical Center, Tel-Hashomer, Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel. 2. Department of General Surgery and Transplantation, Sheba Medical Center, Tel-Hashomer, Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel. niv.pencovich1@gmail.com. 3. Department of Radiology, Sheba Medical Center, Tel-Hashomer, Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Abstract
PURPOSE: Intraoperative ultrasonography (IOUS) of the liver is a crucial adjunct in every liver resection and may significantly impact intraoperative surgical decisions. However, IOUS is highly operator dependent and has a steep learning curve. We describe the design and assessment of an artificial intelligence (AI) system to identify focal liver lesions in IOUS. METHODS: IOUS images were collected during liver resections performed between November 2020 and November 2021. The images were labeled by radiologists and surgeons as normal liver tissue versus images that contain liver lesions. A convolutional neural network (CNN) was trained and tested to classify images based on the labeling. Algorithm performance was tested in terms of area under the curves (AUCs), accuracy, sensitivity, specificity, F1 score, positive predictive value, and negative predictive value. RESULTS: Overall, the dataset included 5043 IOUS images from 16 patients. Of these, 2576 were labeled as normal liver tissue and 2467 as containing focal liver lesions. Training and testing image sets were taken from different patients. Network performance area under the curve (AUC) was 80.2 ± 2.9%, and the overall classification accuracy was 74.6% ± 3.1%. For maximal sensitivity of 99%, the classification specificity is 36.4 ± 9.4%. CONCLUSIONS: This study provides for the first time a proof of concept for the use of AI in IOUS and show that high accuracy can be achieved. Further studies using high volume data are warranted to increase accuracy and differentiate between lesion types.
PURPOSE: Intraoperative ultrasonography (IOUS) of the liver is a crucial adjunct in every liver resection and may significantly impact intraoperative surgical decisions. However, IOUS is highly operator dependent and has a steep learning curve. We describe the design and assessment of an artificial intelligence (AI) system to identify focal liver lesions in IOUS. METHODS: IOUS images were collected during liver resections performed between November 2020 and November 2021. The images were labeled by radiologists and surgeons as normal liver tissue versus images that contain liver lesions. A convolutional neural network (CNN) was trained and tested to classify images based on the labeling. Algorithm performance was tested in terms of area under the curves (AUCs), accuracy, sensitivity, specificity, F1 score, positive predictive value, and negative predictive value. RESULTS: Overall, the dataset included 5043 IOUS images from 16 patients. Of these, 2576 were labeled as normal liver tissue and 2467 as containing focal liver lesions. Training and testing image sets were taken from different patients. Network performance area under the curve (AUC) was 80.2 ± 2.9%, and the overall classification accuracy was 74.6% ± 3.1%. For maximal sensitivity of 99%, the classification specificity is 36.4 ± 9.4%. CONCLUSIONS: This study provides for the first time a proof of concept for the use of AI in IOUS and show that high accuracy can be achieved. Further studies using high volume data are warranted to increase accuracy and differentiate between lesion types.
Authors: Mark G van Vledder; Timothy M Pawlik; Sanjay Munireddy; Ulrike Hamper; Mechteld C de Jong; Michael A Choti Journal: Ann Surg Oncol Date: 2010-06-02 Impact factor: 5.344
Authors: Anastasia Pazaiti; Athanasia M Pazaitis; Konstantinos G Toutouzas; Dimitris I Papadimitriou; Basil D Papadopoulos; Lila S Papacostas; Kyriakos A Strigaris; John D Papadimitriou Journal: Int Surg Date: 2009 Jan-Feb
Authors: Courtney L Scaife; Chaan S Ng; Lee M Ellis; J Nicolas Vauthey; Chuslip Charnsangavej; Steven A Curley Journal: Ann Surg Oncol Date: 2006-02-15 Impact factor: 5.344