Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors: A meta‐analysis

Ye, Xiaohua; Zhao, Linlin; Wang, Lei

doi:10.1111/1751-2980.13110

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…The availability of such data sets can be a limitation, particularly for rare pancreatic conditions. Most studies that have been done are retrospective and use a limited number of images from single-centre studies 19 , 26 , 36 – 38 . Due to limited data availability and concerns related to model over-fitting, the effectiveness of these systems remains insufficient, emphasizing the need for comprehensive external validation 39 .…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space occupying lesion: A systematic review and meta-analysis

Dhali,

Kipkorir,

Srichawla

et al. 2023

International Journal of Surgery

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Background: Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce inter-observer variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. Methods: Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. Results: A total of 21 studies were included in the review with 4 studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity (P<0.05) in the Cochrane’s Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane’s Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane’s Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%). Conclusion: AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space occupying lesion: A systematic review and meta-analysis

Dhali,

Kipkorir,

Srichawla

et al. 2023

International Journal of Surgery

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“…AI based on deep-learning technology has progressed tremendously in recent years and it has already been applied to assist EUS for pancreatic diseases [13][14][15][16] and GI SELs, in particular, to differentiate gastrointestinal stromal tumors (GIST) from other SELs [17][18][19]. In this setting, AI is a promising tool, but we need further evidence.…”

Section: Artificial Intelligencementioning

confidence: 99%

Diagnostic Endoscopic Ultrasound (EUS) of the Luminal Gastrointestinal Tract

Impellizzeri,

Donato,

De Angelis

et al. 2024

Diagnostics

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The purpose of this review is to focus on the diagnostic endoscopic ultrasound of the gastrointestinal tract. In the last decades, EUS has gained a central role in the staging of epithelial and sub-epithelial lesions of the gastrointestinal tract. With the evolution of imaging, the position of EUS in the diagnostic work-up and the staging flow-chart has continuously changed with two extreme positions: some gastroenterologists think that EUS is absolutely indispensable, and some think it is utterly useless. The truth is, as always, somewhere in between the two extremes. Analyzing the most up-to-date and strong evidence, we will try to give EUS the correct position in our daily practice.

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“…For instance, a feature that squeezes problems reduces the complexity of the input space, making it less likely to be subjected to system problems [142]. Another proposed method involves incorporating problematic case examples into the system's training data [143,144].…”

Section: Acceptance Of Aimentioning

confidence: 99%

Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

Alzubaidi,

Al-Sabaawi,

Bai

et al. 2023

International Journal of Intelligent Systems

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Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

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Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors: A meta‐analysis

Cited by 9 publications

References 40 publications

Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space occupying lesion: A systematic review and meta-analysis

Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space occupying lesion: A systematic review and meta-analysis

Diagnostic Endoscopic Ultrasound (EUS) of the Luminal Gastrointestinal Tract

Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

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