Scientific publications

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

Feb 5, 2024 | Magazine: Surgical Endoscopy

Victor Lopez-Lopez  1 , Zeniche Morise  2 , Mariano Albadalejo-González  3 , Concepción Gomez Gavara  4 , Brian K P Goh  5   6 , Ye Xin Koh  5   6 , Sijberden Jasper Paul  7 , Mohammed Abu Hilal  7   8 , Kohei Mishima  9 , Jaime Arthur Pirola Krürger  10 , Paulo Herman  10 , Alvaro Cerezuela  1 , Roberto Brusadin  1 , Takashi Kaizu  11 , Juan Lujan  1   12 , Fernando Rotellar  12 , Kazuteru Monden  13 , Mar Dalmau  4 , Naoto Gotohda  14 , Masashi Kudo  14 , Akishige Kanazawa  15 , Yutaro Kato  16 , Hiroyuki Nitta  17 , Satoshi Amano  17 , Raffaele Dalla Valle  18 , Mario Giuffrida  18 , Masaki Ueno  19 , Yuichiro Otsuka  20 , Daisuke Asano  21 , Minoru Tanabe  21 , Osamu Itano  22 , Takuya Minagawa  22 , Dilmurodjon Eshmuminov  23 , Irene Herrero  24 , Pablo Ramírez  1 , José A Ruipérez-Valiente  25 , Ricardo Robles-Campos #  1 , Go Wakabayashi #  9


Background: Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8.

Methods: We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open.

Results: Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time."

Conclusion: We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

CITATION  Surg Endosc. 2024 Feb 5. doi: 10.1007/s00464-024-10681-6