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