A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos) - 14/07/23
, Wu-Jun Li, PhD 3, 4, 8, ⁎ , Lei Wang, MD, PhD 1, 2, ⁎ Abstract |
Background and Aims |
It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. This study aimed to develop a real-time interpretable artificial intelligence (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC).
Methods |
A novel interpretable AI system called MBSDeiT was developed consisting of 2 models to identify qualified images and then predict MBSs in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, and prospective testing data sets and subgroup analyses, and at the video level on the prospective data sets; these findings were compared with those of the endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the interpretability.
Results |
MBSDeiT can first automatically select qualified DSOC images with an area under the curve (AUC) of .963 and .968 to .973 on the internal testing data set and the external testing data sets, and then identify MBSs with an AUC of .971 on the internal testing data set, an AUC of .978 to .999 on the external testing data sets, and an AUC of .976 on the prospective testing data set, respectively. MBSDeiT accurately identified 92.3% of MBSs in prospective testing videos. Subgroup analyses confirmed the stability and robustness of MBSDeiT. The AI system achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with 4 endoscopic features (nodular mass, friability, raised intraductal lesion, and abnormal vessels; P < .05) under DSOC, which is consistent with the endoscopists’ predictions.
Conclusions |
The study findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBSs under DSOC.
Il testo completo di questo articolo è disponibile in PDF.Graphical abstract |
Abbreviations : AI, AUC, CI, DeiT, DSOC, MBS, MLP, NA, NJDTH, NPV, PPV, QLHSU, VI, ViT, WXPH
Mappa
| DISCLOSURE: All authors disclosed no financial relationships. The project is funded by China Postdoctoral Science Foundation (2022M721571), Jiangsu Provincial Health Commission (M20200034), and the key project of medical science and technology development of Nanjing Municipal Health Commission (ZKX21032). The funders of the study played no role in the study design, data collection, analysis, interpretation, or writing of the study. |
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| DIVERSITY, EQUITY, AND INCLUSION: We worked to ensure gender balance in the recruitment of human subjects. While citing references scientifically relevant for this work, we actively worked to promote gender balance in our reference list. The author list of this paper includes contributors from the location where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. |
Vol 98 - N° 2
P. 199 - agosto 2023 Ritorno al numero
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