TY - JOUR
T1 - Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8
T2 - an international multicenter study
AU - Lopez-Lopez, Victor
AU - Morise, Zeniche
AU - Albaladejo-González, Mariano
AU - Gavara, Concepción Gomez
AU - Goh, Brian K.P.
AU - Koh, Ye Xin
AU - Paul, Sijberden Jasper
AU - Hilal, Mohammed Abu
AU - Mishima, Kohei
AU - Krürger, Jaime Arthur Pirola
AU - Herman, Paulo
AU - Cerezuela, Alvaro
AU - Brusadin, Roberto
AU - Kaizu, Takashi
AU - Lujan, Juan
AU - Rotellar, Fernando
AU - Monden, Kazuteru
AU - Dalmau, Mar
AU - Gotohda, Naoto
AU - Kudo, Masashi
AU - Kanazawa, Akishige
AU - Kato, Yutaro
AU - Nitta, Hiroyuki
AU - Amano, Satoshi
AU - Valle, Raffaele Dalla
AU - Giuffrida, Mario
AU - Ueno, Masaki
AU - Otsuka, Yuichiro
AU - Asano, Daisuke
AU - Tanabe, Minoru
AU - Itano, Osamu
AU - Minagawa, Takuya
AU - Eshmuminov, Dilmurodjon
AU - Herrero, Irene
AU - Ramírez, Pablo
AU - Ruipérez-Valiente, José A.
AU - Robles-Campos, Ricardo
AU - Wakabayashi, Go
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Liver resection
KW - Making decision
KW - Minimally invasive surgery
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U2 - 10.1007/s00464-024-10681-6
DO - 10.1007/s00464-024-10681-6
M3 - Article
C2 - 38315197
AN - SCOPUS:85184251088
SN - 0930-2794
JO - Surgical endoscopy
JF - Surgical endoscopy
ER -