Artificial intelligence in endoscopic imaging for detection of malignant biliary strictures and cholangiocarcinoma: a systematic review

Njei, Basile

doi:10.20524/aog.2023.0779

Cited by 6 publications

(8 citation statements)

References 25 publications

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“…In terms of healthcare, however, indeterminate biliary strictures with the spyglass DSOC image datasets were not publicly available. All studies in this task benchmarked only private datasets [39].…”

Section: Experimental Setup a Datasetmentioning

confidence: 99%

Real-Time Identification of Malignant Biliary Strictures on Cholangioscopy Images Using Explainable Convolutional Neural Networks With Heatmaps

Phuangthongkham,

Angsuwatcharakon,

Kulpatcharapong

et al. 2023

IEEE Access

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Determination of benign or malignant etiology of bile duct strictures is difficult. Currently, a digital single-operator cholangioscopy allows endoscopists to examine the bile duct with greater accuracy. Consequently, lesions in the bile duct can be detected directly with their own eyes. However, there is still inconsistency of diagnosis among endoscopists. Therefore, a biopsy is usually considered as a gold standard. Thus, pieces of tissue are taken from the bile duct and undergo examination. A false negative of cancer diagnosis from a sampling error of biopsy leads to the need to repeat procedures. In this work, we propose a convolutional neural network specifically designed for classifying malignant biliary strictures in real-time. In our development, this model can generate output not only for classification but also for a class activation map, which relies solely on an image-level label rather than annotation position. The model mainly focuses on tissues, not on equipment e.g. guide wire. To increase the number of cholangioscopy images, and solve the issue of equipment, ''a guide-wire augmentation'' is invented. To employ a video inference, our model for still images is further modified. In our experiment, all models are run on 3 patient-based bootstraps. The data contains 104 patients' records collected at King Chulalongkorn Memorial Hospital with 885 images: 447 malignant and 438 benign images. The model's performance for still images is 0.8577 and 0.8395 in terms of sensitivity and F1, respectively. With our video inference algorithm, the sensitivity of the model attains 0.9024 and the F1-score achieves 0.9193. Finally, the model can facilitate real-time inference with a speed of 83 frames per second. INDEX TERMS Deep learning, indeterminate biliary strictures, real-time image classification, explainable deep learning.The associate editor coordinating the review of this manuscript and approving it for publication was Carmelo Militello .

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Section: Experimental Setup a Datasetmentioning

confidence: 99%

Real-Time Identification of Malignant Biliary Strictures on Cholangioscopy Images Using Explainable Convolutional Neural Networks With Heatmaps

Phuangthongkham,

Angsuwatcharakon,

Kulpatcharapong

et al. 2023

IEEE Access

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“…The differentiation between malignant and benign biliary strictures remains difficult, despite advances in endoscopic techniques. Conventional ERCP techniques, involving cholangiogram appearance and brush cytology, often fail to differentiate reliably between malignant and benign biliary strictures because of their limited sensitivity [ 73 , 74 ]. Probe-based confocal laser endomicroscopy (pCLE) represents a novel imaging technique offering “optical biopsies” and providing in vivo cellular-level architectural information.…”

Section: Introductionmentioning

confidence: 99%

“…Cholangioscopy, especially with the advent of single-operator systems, has significantly advanced the diagnostic capabilities for biliary strictures. Endoscopic-ultrasound-guided fine-needle aspiration (EUS-FNA) has been shown to be more effective than conventional ERCP techniques in diagnosing malignant biliary strictures, with higher sensitivity and accuracy [ 73 , 74 ]. However, it is crucial to note that negative results from these techniques do not completely exclude malignancy, given their low negative predictive value.…”

Section: Introductionmentioning

confidence: 99%

“…By integrating AI with pCLE, cholangioscopy and EUS-FNA data, it may be possible to enhance the diagnostic accuracy for IDBS and pancreatic cancer. This integration could lead to more reliable differentiation between malignant and benign strictures, better informing treatment decisions, and improving patient outcomes [ 73 , 74 ].…”

Section: Introductionmentioning

confidence: 99%

“…Cholangiocarcinoma is notoriously difficult to diagnose early, since traditional techniques like ERCP have limited sensitivity [ 73 ]. With its ability to process and analyze complex image data at an unprecedented scale, AI presents a viable solution to this challenge.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in gastrointestinal endoscopy: a comprehensive review

Ali

2024

aog

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Integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a significant leap forward in managing GI disorders. AI-enabled applications, such as computer-aided detection and computer-aided diagnosis, have significantly advanced GI endoscopy, improving early detection, diagnosis and personalized treatment planning. AI algorithms have shown promise in the analysis of endoscopic data, critical in conditions with traditionally low diagnostic sensitivity, such as indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks can markedly improve the diagnostic process when integrated with cholangioscopy or endoscopic ultrasound, especially in the detection of malignant biliary strictures and cholangiocarcinoma. AI’s capacity to analyze complex image data and offer real-time feedback can streamline endoscopic procedures, reduce the need for invasive biopsies, and decrease associated adverse events. However, the clinical implementation of AI faces challenges, including data quality issues and the risk of overfitting, underscoring the need for further research and validation. As the technology matures, AI is poised to become an indispensable tool in the gastroenterologist’s arsenal, necessitating the integration of robust, validated AI applications into routine clinical practice. Despite remarkable advances, challenges such as operator-dependent accuracy and the need for intricate examinations persist. This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.

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