Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study

Saraiva, Miguel Mascarenhas; Ribeiro, Tiago; Ferreira, João; Vilas-Boas, Filipe; Afonso, João; Santos, Ana Luísa; Parente, Marco; Jorge, Renato Natal; Pereira, Pedro; Macedo, Guilherme

doi:10.1016/j.gie.2021.08.027

Cited by 38 publications

(27 citation statements)

References 24 publications

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“…Therefore, a targeted biopsy is required for precise assessment of indeterminate biliary strictures [20,21]. The diagnostic classification should be established for the generalizability of digital POCS diagnosis [22 ▪▪ ,23 ▪ ], and artificial intelligence may help improve the diagnostic yield of POCS [24 ▪ ].…”

Section: Diagnosis Of Biliary Stricturesmentioning

confidence: 99%

Current endoscopic approaches to biliary strictures

Shimoda

Nakai

Fujishiro

2022

Current Opinion in Gastroenterology

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Purpose of reviewTo summarize the current status and future perspectives of the endoscopic management of biliary strictures.Recent findingsIn addition to conventional diagnostic modalities, such as cross-sectional imaging and endoscopic ultrasonography (EUS), per-oral cholangioscopy is helpful for indeterminate biliary strictures. It allows direct visualization of the biliary tract and targeted biopsy. For distal malignant biliary obstruction (MBO), a self-expandable metal stent (SEMS) via endoscopic retrograde cholangiopancreatography (ERCP) is a standard of care. EUS-guided biliary drainage (EUS-BD) is an emerging alternative to percutaneous transhepatic biliary drainage in cases with failed ERCP. EUS-BD is also an effective salvage option for perihilar MBO, which can not be managed via ERCP or percutaneous transhepatic biliary drainage. Preoperative drainage is necessary for most jaundiced patients as neoadjuvant chemotherapy is widely administered for resectable and borderline resectable pancreatic cancer, and a SEMS is preferred in this setting, too. For benign biliary strictures, a covered SEMS can improve stricture resolution and reduce the number of endoscopic sessions as compared to plastic stents.SummaryERCP and EUS play a central role in the diagnosis and drainage for both malignant and benign biliary strictures.

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Section: Diagnosis Of Biliary Stricturesmentioning

confidence: 99%

Current endoscopic approaches to biliary strictures

Shimoda

Nakai

Fujishiro

2022

Current Opinion in Gastroenterology

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“…For stage II, the x required neoplastic or nonneoplastic cases was calculated using the power.diagnostic.test function in the MKmisc (v1.6; Kohl M, 2019) package [18]. Assumption for the proportion of discordant pairs considered a 94.7% sensitivity for a CNN model, as estimated by Saraiva M et al [16].. A probabilistic sample was designed considering a 10% delta and a 50% prevalence (1:1 case vs. controls ratio, to recreate the same probability of neoplastic or non-neoplastic cases during DSOC videos assessment, as in Bernoulli trial). A 5% significance level was considered, along with a defined 5% and 20% alpha and beta error, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…To date, despite the numerous advantages of DSOC, there is an ongoing discrepancy between the operators' visual impression using current classifications for indeterminate biliary lesions. To overcome this limitation, the application of new technologies to aid in image interpretation has been proposed; however, the proposed models could only be applied in images [15,16]. In the present study, we developed a new DSOC-based CNN for recognizing neoplasia in indeterminate biliary lesions in prerecorded videos and real-time DSOC procedures and compared the model with DSOC experts and nonexperts using the CRM and Mendoza classifications.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model

et al. 2023

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Background and study aim: We aimed to develop a convolutional neural network (CNN) model for detecting neoplastic lesions during real-time digital single-operator cholangioscopy (DSOC) and clinically validated the model through comparisons with DSOC experts and nonexpert endoscopists. Patients and methods: In this two-stage study, we first developed and validated CNN1. Then, we performed a multicenter diagnostic trial to compare four DSOC experts and nonexperts against an improved model (CNN2). They classified the lesions in neoplastic and nonneoplastic in accordance with Carlos Robles-Medranda (CRM) and Mendoza disaggregated criteria. The final diagnosis of neoplasia was based on histopathology and twelve-month follow-up outcomes. Results: In stage I, CNN2 achieved a mAP of 0.88, an IoU of 83.24%, and a total loss of 0.0975. For clinical validation, a total of 170 videos from newly included patients were analyzed with the CNN2. 50% of cases had neoplastic lesions. This model achieved significant accuracy values for neoplastic diagnosis cases, with a 90.5% sensitivity, 68.2% specificity, and 74.0% and 87.8% positive and negative predictive values, respectively. CNN2 model outperformed nonexpert #2 ([ROC-CRM: 0.657 vs ROC-CNN2: 0.794, P<0.05 and ROC-Mendoza: 0.657 vs ROC-CNN2: 0.794, P<0.05]), nonexpert #4 (ROC-CRM: 0.683 vs ROC-CNN2: 0.791, P<0.05), and expert #4 ([ROC-CRM: 0.755 vs ROC-CNN2: 0.848, P<0,05 and ROC-Mendoza: 0.753 vs ROC-CNN2: 0.848, P<0,05]). Conclusions: The proposed CNN models distinguish neoplastic bile duct lesions with good accuracy and outperformed two nonexperts and one expert endoscopists.

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“…In the last decade, we assisted the development of AI systems for application to several diagnostic modalities. Studies on the implementation of CNNs to several endoscopic modalities produced promising results [11][12][13][14][15]. In this retrospective study, we developed a pioneer deep learning algorithm for automatic detection of gastrointestinal angioectasia, with high sensitivity, specificity and overall accuracy.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

et al. 2021

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Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

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Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study

Cited by 38 publications

References 24 publications

Current endoscopic approaches to biliary strictures

Current endoscopic approaches to biliary strictures

Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

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