Machine learning for medical ultrasound: status, methods, and future opportunities.
Laura J. Brattain,Brian A. Telfer,Manish Dhyani,Manish Dhyani,Joseph R. Grajo,Anthony E. Samir +5 more
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TLDR
Leading machine learning approaches and research directions in US are reviewed, with an emphasis on recent ML advances, and an outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization is presented.Abstract:
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.read more
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Journal ArticleDOI
Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.
Aaron E. Kornblith,Newton Addo,Ruolei Dong,Ruolei Dong,Robert Rogers,Jacqueline Grupp-Phelan,Atul J. Butte,Pavan Gupta,Rachael A. Callcut,Rachael A. Callcut,Rima Arnaout +10 more
TL;DR: In this article, the authors developed and conducted a retrospective cohort analysis of a deep learning view classifier on real-world FAST studies performed on injured children less than 18 years old in two pediatric emergency departments by 30 different clinicians.
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A mutual promotion encoder-decoder method for ultrasonic hydronephrosis diagnosis.
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Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound.
Chih Jui Yu,Hsing Jung Yeh,Chun Chao Chang,Chun Chao Chang,Jui Hsiang Tang,Wei Yu Kao,Wei Yu Kao,Wen Chao Chen,Yi Jin Huang,Chien Hung Li,Wei Hao Chang,Yun Ting Lin,Herdiantri Sufriyana,Emily Chia Yu Su,Emily Chia Yu Su +14 more
TL;DR: In this article, a machine learning system was developed to detect and localize gallstones and to detect acute cholecystitis by ultrasound (US) still images taken by physicians or technicians for preliminary diagnoses.
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Imaging Biomarker Knowledge Transfer for Attention-Based Diagnosis of COVID-19 in Lung Ultrasound Videos
Tyler Lum,Mobina Mahdavi,Oron Frenkel,Christopher Lee,Mohammad H. Jafari,Fatemeh Taheri Dezaki,Nathan Van Woudenberg,Ang Nan Gu,Purang Abolmaesumi,Teresa Tsang +9 more
TL;DR: In this paper, an attention-based video model was proposed to detect lung disease signatures in lung ultrasound images and leverage a knowledge transfer approach to overcome existing limitations in data availability, which achieved 80% precision and 87% recall for COVID-19.
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