BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications

Intharah, Thanapong; Wiratchawa, Kannika; Wanna, Yupaporn; Junsawang, Prem; Titapun, Attapol; Techasen, Anchalee; Boonrod, Arunnit; Laopaiboon, Vallop; Chamadol, Nittaya; Khuntikeo, Narong

doi:10.1016/j.artmed.2023.102539

Artificial Intelligence in Medicine

2023

DOI: 10.1016/j.artmed.2023.102539

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BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications

Thanapong Intharah

Kannika Wiratchawa

Yupaporn Wanna

et al.

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2024

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Cited by 2 publications

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Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications

Cai,

Pfob

2024

Abdom Radiol

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Background In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-art applications for AI in abdominal and pelvic ultrasound imaging. Methods We searched the PubMed, FDA, and ClinicalTrials.gov databases for applications of AI in abdominal and pelvic ultrasound imaging. Results A total of 128 titles were identified from the database search and were eligible for screening. After screening, 57 manuscripts were included in the final review. The main anatomical applications included multi-organ detection (n = 16, 28%), gynecology (n = 15, 26%), hepatobiliary system (n = 13, 23%), and musculoskeletal (n = 8, 14%). The main methodological applications included deep learning (n = 37, 65%), machine learning (n = 13, 23%), natural language processing (n = 5, 9%), and robots (n = 2, 4%). The majority of the studies were single-center (n = 43, 75%) and retrospective (n = 56, 98%). We identified 17 FDA approved AI ultrasound devices, with only a few being specifically used for abdominal/pelvic imaging (infertility monitoring and follicle development). Conclusion The application of AI in abdominal/pelvic ultrasound shows promising early results for disease diagnosis, monitoring, and report refinement. However, the risk of bias remains high because very few of these applications have been prospectively validated (in multi-center studies) or have received FDA clearance.

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Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications

Cai,

Pfob

2024

Abdom Radiol

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Applications of artificial intelligence in biliary tract cancers

Gupta,

Basu,

Arora

2024

Indian J Gastroenterol

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BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications

Cited by 2 publications

References 36 publications

Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications

Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications

Applications of artificial intelligence in biliary tract cancers

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