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Background Artificial intelligence (AI) has the potential to enhance patient safety in surgery, and all its aspects, including education and training, will derive considerable benefit from AI. In the present study, deep-learning models were used to predict the rates of proficiency acquisition in robot-assisted surgery (RAS), thereby providing surgical programs directors information on the levels of the innate ability of trainees to facilitate the implementation of flexible personalized training. Methods 176 medical students, without prior experience with surgical simulators, were trained to reach proficiency in five tasks on a virtual simulator for RAS. Ensemble deep neural networks (DNN) models were developed and compared with other ensemble AI algorithms, i.e., random forests and gradient boosted regression trees (GBRT). Results DNN models achieved a higher accuracy than random forests and GBRT in predicting time to proficiency, 0.84 vs. 0.70 and 0.77, respectively (Peg board 2), 0.83 vs. 0.79 and 0.78 (Ring walk 2), 0.81 vs 0.81 and 0.80 (Match board 1), 0.79 vs. 0.75 and 0.71 (Ring and rail 2), and 0.87 vs. 0.86 and 0.84 (Thread the rings 2). Ensemble DNN models outperformed random forests and GBRT in predicting number of attempts to proficiency, with an accuracy of 0.87 vs. 0.86 and 0.83, respectively (Peg board 2), 0.89 vs. 0.88 and 0.89 (Ring walk 2), 0.91 vs. 0.89 and 0.89 (Match board 1), 0.89 vs. 0.87 and 0.83 (Ring and rail 2), and 0.96 vs. 0.94 and 0.94 (Thread the rings 2). Conclusions Ensemble DNN models can identify at an early stage the acquisition rates of surgical technical proficiency of trainees and identify those struggling to reach the required expected proficiency level.

Intraductal papillary mucinous neoplasms (IPMNs) are non-obligatory precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The identification of molecular biomarkers able to predict the risk of progression of IPMNs toward malignancy is largely lacking and sorely needed. Telomere length (TL) is associated with the susceptibility of developing cancers, including PDAC. Moreover, several PDAC risk factors have been shown to be associated with IPMN transition to malignancy. TL is genetically determined, and the aim of this study was to use 11 SNPs, alone or combined in a score (teloscore), to estimate the causal relation between genetically determined TL and IPMNs progression. For this purpose, 173 IPMN patients under surveillance were investigated. The teloscore did not show any correlation, however, we observed an association between PXK-rs6772228-A and an increased risk of IPMN transition to malignancy (HR=3.17; 95%CI 1.47-6.84; P=3.24x10 -3). This effect was also observed in a validation cohort of 142 IPMNs even though the association was not statistically significant. The combined analysis was consistent showing an association between PXK-rs6772228-A and increased risk of progression. The A allele of this SNP is strongly associated with shorter LTL that in turn have been reported to be associated with increased risk of developing PDAC. These results clearly highlight the importance of looking for genetic variants as potential biomarkers in this setting in order to further our understanding the etiopathogenesis of PDAC and suggest that genetically determined TL might be an additional marker of IPMN prognosis.