Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images

Zhou, Wenying; Yang, Yang; Yu, Cheng; Liu, Juxian; Duan, Xingxing; Weng, Zongjie; Chen, Dan; Liang, Qianhong; Qin, Fang; Zhou, Jiaojiao; Ju, Hehua; Luo, Zhenhua; Guo, Weihao; Ma, Xiaoyan; Xie, Xian-Jin; Wang, Ruixuan; Zhou, Luyao

doi:10.1038/s41467-021-21466-z

Cited by 71 publications

(56 citation statements)

References 38 publications

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“…A similar ML application with the same purpose was made by Angelico et al, who created PopòApp [ 56 ]. Moreover, Zhou et al developed an ensembled deep learning model to facilitate the diagnosis of BA for non-expert radiologists using DB values and US images as well as videos of the gallbladder [ 57 ]. In this setting, this pilot experience is the first that reports an ML evaluation using laboratory and imaging parameters with long-term predictive purposes in patients with BA after KP, supporting the main role of laboratory tests in the follow-up of such patients.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

et al. 2021

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Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

et al. 2021

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“…Recently, an ensemble deep learning model from US gallbladder images has been developed, which adopted two types of effective AI techniques called deep convolutional neural networks (CNNs) and ensemble learning [ 67 ]. In this study, the training cohort was randomly separated into five complementary subsets, four of which were used each time to train a CNN, and the remaining subset was used for validation.…”

Section: Artificial Intelligence Based On Us Gallbladder Imagesmentioning

confidence: 99%

Ultrasound for the Diagnosis of Biliary Atresia: From Conventional Ultrasound to Artificial Intelligence

Zhou

2021

Diagnostics

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Biliary atresia is an aggressive liver disease of infancy and can cause death without timely surgical intervention. Early diagnosis of biliary atresia is critical to the recovery of bile drainage and long-term transplant-free survival. Ultrasound is recommended as the initial imaging strategy for the diagnosis of biliary atresia. Numerous ultrasound features have been proved helpful for the diagnosis of biliary atresia. In recent years, with the help of new technologies such as elastography ultrasound, contrast-enhanced ultrasound and artificial intelligence, the diagnostic performance of ultrasound has been significantly improved. In this review, various ultrasound features in the diagnosis of biliary atresia are summarized. A diagnostic decision flow chart for biliary atresia is proposed on the basis of the hybrid technologies, combining conventional ultrasound, elastography and contrast-enhanced ultrasound. In addition, the application of artificial intelligence in the diagnosis of biliary atresia with ultrasound images is also introduced.

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“…A prospective five-center study using DL from ultrasound videos of biliary atresia achieved higher diagnostic accuracy than human experts. The research team has also developed a mobile APP by DL of ultrasound pictures, enabling rural doctors in remote areas to perform CAD by taking and uploading photographs of suspected biliary atresia [ 66 ].…”

Section: Application Of DL In Digestive System Imagingmentioning

confidence: 99%

A systematic review on application of deep learning in digestive system image processing

2021

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Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images

Cited by 71 publications

References 38 publications

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

Ultrasound for the Diagnosis of Biliary Atresia: From Conventional Ultrasound to Artificial Intelligence

A systematic review on application of deep learning in digestive system image processing

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