Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network

Islam, Mohaimenul; Poly, Tahmina Nasrin; Walther, Bruno; Lin, Ming‐Chin; Li, Yu-Chuan

doi:10.3390/cancers13215253

Cited by 8 publications

(7 citation statements)

References 53 publications

(54 reference statements)

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“…Fang et al [ 53 ] revealed the AUC, SEN, and SPE of CNN in the endoscopic image-based GC diagnosis were 0.89, 0.83, and 0.94. Md Mohaimenul Islam et al [ 54 ] revealed that the SROC and SEN of the CNN model in EGC diagnosis were 0.95 and 0.89, respectively. Among the articles included, only 2 articles [ 25 , 26 ] used conventional ML methods (SVM).…”

Section: Discussionmentioning

confidence: 99%

The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis

Shi,

Fan,

et al. 2024

World J Surg Onc

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Background The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis. Methods We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed. Results Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87–0.94), 0.85 (95% CI: 0.81–0.89), and 0.94 (95% CI: 0.39–1.00) in the training set and 0.90 (95% CI: 0.86–0.93), 0.90 (95% CI: 0.86–0.92), and 0.96 (95% CI: 0.19–1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56–0.71), 0.84 (95% CI: 0.77–0.89), and 0.80 (95% CI: 0.29–0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74–0.85), 0.88 (95% CI: 0.85–0.91), and 0.91 (95% CI: 0.37–0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64). Conclusion ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.

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Section: Discussionmentioning

confidence: 99%

The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis

Shi,

Fan,

et al. 2024

World J Surg Onc

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“…In recent years, CNN has made remarkable progress in the field of endoscopy, including gastrointestinal disorders ( 7 , 8 , 27 – 31 ). Computer-aided diagnosis studies in upper endoscopy were relatively well established ( 8 , 29 – 31 ). With derived image data, CNN can facilitate endoscopists with diagnostic and therapeutic interventions by recognizing image features.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Evaluation of Helicobacter pylori Infection by Convolution Neural Network During White-Light Endoscopy: A Prospective, Multicenter Study (With Video)

Shen,

Chen,

Zhang

et al. 2023

Clin Transl Gastroenterol

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Objectives: Convolutional neural network (CNN) during endoscopy may facilitate evaluation of Helicobacter pylori infection without obtaining gastric biopsies. The aim of the study was to evaluate the diagnosis accuracy of a computer-aided decision support system for H. pylori infection (CADSS-HP) based on CNN under white-light endoscopy. Methods: Archived video recordings of upper endoscopy with white-light examinations performed at Sir Run Run Shaw Hospital (January 2019-September 2020) were utilized to develop CADSS-HP. Patients receiving endoscopy were prospectively enrolled (August 2021-August 2022) from three centers to calculate the diagnostic property. Accuracy of CADSS-HP for H. pylori infection was also compared to endoscopic impression, urea breath test (URT) and histopathology. H. pylori infection was defined by positive test on histopathology and/or URT. Results: Video recordings of 599 patients who received endoscopy were utilized to develop CADSS-HP. Subsequently, 456 patients participated in the prospective evaluation including 189 (41.4%) with H. pylori infection. With a threshold of 0.5, CADSS-HP achieved an area under the curve (AUC) of 0.95 (95% confidence interval [CI], 0.93-0.97) with sensitivity and specificity of 91.5% (95%CI 86.4%-94.9%) and 88.8% (95%CI 84.2%-92.2%), respectively. CADSS-HP demonstrated higher sensitivity (91.5% vs 78.3%; mean difference=13.2%, 95%CI 5.7%-20.7%) and accuracy (89.9% vs 83.8%, mean difference=6.1%, 95%CI 1.6%-10.7%) compared to endoscopic diagnosis by endoscopists. Sensitivity of CADSS-HP in diagnosing H. pylori was comparable to URT (91.5% vs 95.2%; mean difference=3.7%, 95%CI -1.8%-9.4%), better than histopathology (91.5% vs 82.0%; mean difference=9.5%, 95%CI 2.3%-16.8%). Conclusions: CADSS-HP achieved high sensitivity in the diagnosis of H. pylori infection in the real-time test, outperforming endoscopic diagnosis by endoscopists and comparable to URT. Clinicaltrials.gov; ChiCTR2000030724.

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“…Currently, the use of artificial intelligence (AI) has reached widespread popularity at an unprecedented rate [ 1 ]. AI algorithms have emerged as potential tools in diverse areas of healthcare, including chronic disease management and clinical decision-making [ 2 , 3 , 4 ]. AI is playing a prominent role in dementia research due to advancements in computing power, novel algorithms, and the availability of big data generated from medical health records and wearable devices [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Dementia: A Bibliometric Study

Islam

et al. 2023

Diagnostics

Self Cite

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The applications of artificial intelligence (AI) in dementia research have garnered significant attention, prompting the planning of various research endeavors in current and future studies. The objective of this study is to provide a comprehensive overview of the research landscape regarding AI and dementia within scholarly publications and to suggest further studies for this emerging research field. A search was conducted in the Web of Science database to collect all relevant and highly cited articles on AI-related dementia research published in English until 16 May 2023. Utilizing bibliometric indicators, a search strategy was developed to assess the eligibility of titles, utilizing abstracts and full texts as necessary. The Bibliometrix tool, a statistical package in R, was used to produce and visualize networks depicting the co-occurrence of authors, research institutions, countries, citations, and keywords. We obtained a total of 1094 relevant articles published between 1997 and 2023. The number of annual publications demonstrated an increasing trend over the past 27 years. Journal of Alzheimer’s Disease (39/1094, 3.56%), Frontiers in Aging Neuroscience (38/1094, 3.47%), and Scientific Reports (26/1094, 2.37%) were the most common journals for this domain. The United States (283/1094, 25.86%), China (222/1094, 20.29%), India (150/1094, 13.71%), and England (96/1094, 8.77%) were the most productive countries of origin. In terms of institutions, Boston University, Columbia University, and the University of Granada demonstrated the highest productivity. As for author contributions, Gorriz JM, Ramirez J, and Salas-Gonzalez D were the most active researchers. While the initial period saw a relatively low number of articles focusing on AI applications for dementia, there has been a noticeable upsurge in research within this domain in recent years (2018–2023). The present analysis sheds light on the key contributors in terms of researchers, institutions, countries, and trending topics that have propelled the advancement of AI in dementia research. These findings collectively underscore that the integration of AI with conventional treatment approaches enhances the effectiveness of dementia diagnosis, prediction, classification, and monitoring of treatment progress.

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Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network

Cited by 8 publications

References 53 publications

The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis

The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis

Real-Time Evaluation of Helicobacter pylori Infection by Convolution Neural Network During White-Light Endoscopy: A Prospective, Multicenter Study (With Video)

Artificial Intelligence in Dementia: A Bibliometric Study

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