Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications

Ricci Lara MA, Echeveste R, Ferrante E (2022) Addressing fairness in artificial intelligence for medical imaging. Nat Commun 13:4581. https://doi.org/10.1038/s41467-022-32186-3

Article  PubMed  PubMed Central  CAS  Google Scholar 

FDA Health Center for Devices and Radiological. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. FDA 2023; published online Oct. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices.

Chou T-H, Yeh H-J, Chang C-C, et al (2021) Deep learning for abdominal ultrasound: A computer-aided diagnostic system for the severity of fatty liver. J Chin Med Assoc 84:842–850. https://doi.org/10.1097/JCMA.0000000000000585

Article  PubMed  CAS  Google Scholar 

Cha DI, Kang TW, Min JH, et al (2021) Deep learning-based automated quantification of the hepatorenal index for evaluation of fatty liver by ultrasonography. Ultrasonography 40:565–574. https://doi.org/10.14366/usg.20179

Article  PubMed  PubMed Central  Google Scholar 

Yang Y, Liu J, Sun C, et al (2023) Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population. Eur Radiol 33:5894–5906. https://doi.org/10.1007/s00330-023-09515-1

Article  PubMed  CAS  Google Scholar 

Oezdemir I, Wessner CE, Shaw C, et al (2020) Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoembolization Treatment Response. Ultrasound Med Biol 46:2276–2286. https://doi.org/10.1016/j.ultrasmedbio.2020.05.010

Article  PubMed  PubMed Central  Google Scholar 

Qian H, Shen Z, Zhou D, Huang Y (2023) Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer. Front Oncol 13:1209111. https://doi.org/10.3389/fonc.2023.1209111

Article  PubMed  PubMed Central  CAS  Google Scholar 

Karako K, Mihara Y, Arita J, et al (2022) Automated liver tumor detection in abdominal ultrasonography with a modified faster region-based convolutional neural networks (Faster R-CNN) architecture. Hepatobiliary Surg Nutr 11:675–683. https://doi.org/10.21037/hbsn-21-43

Article  PubMed  PubMed Central  Google Scholar 

Yu C-J, Yeh H-J, Chang C-C, et al (2021) Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound. Comput Methods Programs Biomed 211:106382. https://doi.org/10.1016/j.cmpb.2021.106382

Article  PubMed  Google Scholar 

Intharah T, Wiratchawa K, Wanna Y, et al (2023) BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications. Artif Intell Med 139:102539. https://doi.org/10.1016/j.artmed.2023.102539

Article  PubMed  Google Scholar 

Dadoun H, Rousseau A-L, de Kerviler E, et al (2022) Deep Learning for the Detection, Localization, and Characterization of Focal Liver Lesions on Abdominal US Images. Radiol Artif Intell 4:e210110. https://doi.org/10.1148/ryai.210110

Article  PubMed  PubMed Central  Google Scholar 

Kim T, Choi YH, Choi JH, et al (2021) Gallbladder Polyp Classification in Ultrasound Images Using an Ensemble Convolutional Neural Network Model. J Clin Med 10:. https://doi.org/10.3390/jcm10163585

Article  PubMed  PubMed Central  Google Scholar 

Wu M, Yan C, Wang X, et al (2022) Automatic Classification of Hepatic Cystic Echinococcosis Using Ultrasound Images and Deep Learning. J Ultrasound Med 41:163–174. https://doi.org/10.1002/jum.15691

Article  PubMed  Google Scholar 

Wang Z, Kuerban K, Zhou Z, et al (2023) HCEs-Net: Hepatic cystic echinococcosis classification ensemble model based on tree-structured Parzen estimator and snap-shot approach. Med Phys 50:4244–4254. https://doi.org/10.1002/mp.16444

Article  PubMed  Google Scholar 

Kornblith AE, Addo N, Dong R, et al (2022) Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma. J Ultrasound Med 41:1915–1924. https://doi.org/10.1002/jum.15868

Article  PubMed  Google Scholar 

Hernandez-Torres SI, Bedolla C, Berard D, Snider EJ (2023) An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies. Front Bioeng Biotechnol 11:1244616. https://doi.org/10.3389/fbioe.2023.1244616

Article  PubMed  PubMed Central  Google Scholar 

Leo MM, Potter IY, Zahiri M, et al (2023) Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults. J Digit Imaging 36:2035–2050. https://doi.org/10.1007/s10278-023-00845-6

Article  PubMed  PubMed Central  Google Scholar 

Sjogren AR, Leo MM, Feldman J, Gwin JT (2016) Image Segmentation and Machine Learning for Detection of Abdominal Free Fluid in Focused Assessment With Sonography for Trauma Examinations: A Pilot Study. J Ultrasound Med 35:2501–2509. https://doi.org/10.7863/ultra.15.11017

Article  PubMed  PubMed Central  Google Scholar 

Stiel C, Elrod J, Klinke M, et al (2020) The Modified Heidelberg and the AI Appendicitis Score Are Superior to Current Scores in Predicting Appendicitis in Children: A Two-Center Cohort Study. Front Pediatr 8:592892. https://doi.org/10.3389/fped.2020.592892

Article  PubMed  PubMed Central  Google Scholar 

Marcinkevičs R, Reis Wolfertstetter P, Klimiene U, et al (2024) Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. Med Image Anal 91:103042. https://doi.org/10.1016/j.media.2023.103042

Article  PubMed  Google Scholar 

Kanauchi Y, Hashimoto M, Toda N, et al (2023) Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach. Healthcare (Basel) 11:. https://doi.org/10.3390/healthcare11040484

Article  PubMed  Google Scholar 

Li Z, Song C, Huang J, et al (2022) Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images. Gastroenterol Res Pract 2022:9285238. https://doi.org/10.1155/2022/9285238

Article  PubMed  PubMed Central  Google Scholar 

Zhong W, Liao L, Guo X, Wang G (2019) Fetal electrocardiography extraction with residual convolutional encoder-decoder networks. Australas Phys Eng Sci Med 42:1081–1089. https://doi.org/10.1007/s13246-019-00805-x

Article  PubMed  Google Scholar 

Huang P, Su L, Chen S, et al (2019) 2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning. Phys Med Biol 64:185006. https://doi.org/10.1088/1361-6560/ab33db

Article  PubMed  Google Scholar 

Mezheritsky T, Romaguera LV, Le W, Kadoury S (2022) Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy. Med Image Anal 75:102260. https://doi.org/10.1016/j.media.2021.102260

Article  PubMed  Google Scholar 

Yao C, He J, Che H, et al (2022) Feature pyramid self-attention network for respiratory motion prediction in ultrasound image guided surgery. Int J Comput Assist Radiol Surg 17:2349–2356. https://doi.org/10.1007/s11548-022-02697-x

Article  PubMed  Google Scholar 

Chen A, Zhang J, Zhao L, et al (2021) Machine-learning enabled wireless wearable sensors to study individuality of respiratory behaviors. Biosens Bioelectron 173:112799. https://doi.org/10.1016/j.bios.2020.112799

Article  PubMed  CAS  Google Scholar 

Molnár V, Lakner Z, Molnár A, et al (2022) The Predictive Role of Subcutaneous Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea. Life (Basel) 12:. https://doi.org/10.3390/life12101504

Article  PubMed  Google Scholar 

Petrovsky D V, Pustovoyt VI, Nikolsky KS, et al (2022) Tracking Health, Performance and Recovery in Athletes Using Machine Learning. Sports (Basel) 10:. https://doi.org/10.3390/sports10100160

Article  PubMed  Google Scholar 

Saleh A, Laradji IH, Lammie C, et al (2021) A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images. IEEE J Biomed Health Inform 25:3865–3873. https://doi.org/10.1109/JBHI.2021.3085019

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