Artificial Intelligence—The Rising Star in the Field of Gastroenterology and Hepatology

Stan-Ilie, Mădălina; Șandru, Vasile; Constantinescu, Gabriel; Plotogea, Oana-Mihaela; Rinja, Ecaterina; Ţincu, Iulia; Jichitu, Alexandra; Carasel, Adriana Elena; Butuc, Andreea; Popa, Bogdan

doi:10.3390/diagnostics13040662

Cited by 5 publications

(3 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, for endoscopy, only two are used in the gastrointestinal field, and there is no practical application in the urological field yet. Most of the AI-based diagnostic support systems in gastrointestinal endoscopy are for the colon; however, much of the research and development is also being conducted for lower gastrointestinal endoscopy, upper gastrointestinal endoscopy, wireless capsule endoscopy, and ultrasound endoscopy [21]. Diagnostic assistance methods using AI can be broadly classified into computer-aided detection (CADe), which provides physicians with awareness, and computer-aided diagnosis (CADx), which allows them to differentiate between benign and malignant conditions and tissue types.…”

Section: Challenges With Social Implementation Of Cystoscopy Artifici...mentioning

confidence: 99%

Overview of current applications and trends in artificial intelligence for cystoscopy and transurethral resection of bladder tumours

Ikeda,

Nosato

2023

Current Opinion in Urology

View full text Add to dashboard Cite

Purpose of review Accurate preoperative and intraoperative identification and complete resection of bladder cancer is essential. Adequate postoperative follow-up and observation are important to identify early intravesical recurrence or progression. However, the accuracy of diagnosis and treatment is dependent on the knowledge and experience of the physicians. Artificial intelligence (AI) can be an important tool for physicians performing cystoscopies. Recent findings Reports published over the past year and a half have identified an adequate amount of cystoscopy datasets for deep learning, with rich datasets of multiple tumour types including images of flat, carcinoma-in-situ, and elevated lesions, and more diverse applications. In addition to detecting bladder tumours, AI can assist in diagnosing interstitial cystitis. Applications of AI using conventional white-light and also to bladder endoscopy with different image enhancement techniques and manufacturers is underway. A framework has also been proposed to standardise the management of clinical data from cystoscopy to aid education and AI development and to compare with gastrointestinal endoscopic AI. Although real-world clinical applications have lagged, technological developments are progressing. Summary AI-based cystoscopy is likely to become an important tool and is expected to have real-world clinical applications comprehensively linking AI and imaging, data management systems, and clinicians. Video abstract http://links.lww.com/COU/A45

show abstract

Section: Challenges With Social Implementation Of Cystoscopy Artifici...mentioning

confidence: 99%

Overview of current applications and trends in artificial intelligence for cystoscopy and transurethral resection of bladder tumours

Ikeda,

Nosato

2023

Current Opinion in Urology

View full text Add to dashboard Cite

show abstract

“…AI has already revolutionized various aspects of healthcare and is also making great strides in the field of gastroenterology and endoscopy. Researchers have explored the potential of AI in detecting and classifying various diseases such as intestinal polyps, gastric cancer, and inflammatory bowel diseases[ 5 ]. However, in the field of BE, training these AI models was challenging due to the lack of histologically confirmed BE images.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Barrett's esophagus detection: The role of artificial intelligence and its implications

Massironi

2024

World J Gastroenterol

View full text Add to dashboard Cite

Artificial intelligence (AI) is making significant strides in revolutionizing the detection of Barrett's esophagus (BE), a precursor to esophageal adenocarcinoma. In the research article by Tsai et al , researchers utilized endoscopic images to train an AI model, challenging the traditional distinction between endoscopic and histological BE. This approach yielded remarkable results, with the AI system achieving an accuracy of 94.37%, sensitivity of 94.29%, and specificity of 94.44%. The study's extensive dataset enhances the AI model's practicality, offering valuable support to endoscopists by minimizing unnecessary biopsies. However, questions about the applicability to different endoscopic systems remain. The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

show abstract

“…When delving into the field of medical diagnosis, AI-based systems can prove advantageous by replicating the functions of the human brain in simpler tasks and formulating innovative solutions for more complex ones [8]. Artificial Intelligence (AI) significantly reduces errors during colonoscopy by highlighting the specific polyp region where the doctor could focus [9]. The rise of AI and machine learning has laid the basis for profound impact in many areas.…”

Section: Introductionmentioning

confidence: 99%

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Chen,

Ahmad

2023

IEEE Access

View full text Add to dashboard Cite

Colorectal cancer has been one of the leading causes of mortality over the past decade, and colorectal polyps are the leading cause of this disease. Conventional polyp detection techniques are insufficient for proper detection; thus, an efficient detection method is indispensable. In this study, we collected colorectal images from a hospital in Taiwan, annotated the ground truth of polyp locations, and integrated them with a public dataset to create a colonoscopy dataset. Data augmentation techniques are further used to increase the training dataset's diversity to improve the models' detection performance. By developing the comparison system based on the recent state-of-the-art methods (i.e., FasterRCNN, SSD, YOLOv3, and YOLOv4), we compared the measurement metrics and statistically analyzed the performance of the models to identify the significant statistical difference in models' performance. Moreover, we developed and integrated error handling mechanism with each model to discard the false and null predictions. Finally, our model comparison system selects and proposes the best performing deep learning model to detect and classify colorectal polyps. We expect that the proposed model will accurately locate and classify different types of polyps. Eventually, this approach will ensure a valuable medical aid model. INDEX TERMSColorectal cancer (CRC), colorectal polyps, polyp detection, deep learning, data augmentation, error handling.

show abstract

Artificial Intelligence—The Rising Star in the Field of Gastroenterology and Hepatology

Cited by 5 publications

References 91 publications

Overview of current applications and trends in artificial intelligence for cystoscopy and transurethral resection of bladder tumours

Overview of current applications and trends in artificial intelligence for cystoscopy and transurethral resection of bladder tumours

Advancements in Barrett's esophagus detection: The role of artificial intelligence and its implications

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Contact Info

Product

Resources

About