Machine learning for medical ultrasound: status, methods, and future opportunities
Open accessAbstract
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.
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The highlighted studies [20,21,23] focused on specific demographic groups, limiting the generalizability of their results to broader or underrepresented populations for PAS diagnosis.
“The highlighted studies [20,21,23] focused on specific demographic groups, limiting the generalizability of their results to broader or underrepresented populations.”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [21]
The Discussion and Outlook (Section V; page unavailable) says that single-site, single-device ultrasound datasets limit the generalizability of machine-learning models. It does not say that the highlighted studies focused on specific demographic groups or that their results are less generalizable to underrepresented populations for PAS diagnosis. (LLM, conf=0.84)
Experimental — accuracy not yet measuredEnsemble classification, combining multiple models, demonstrated improved accuracy and robustness for PAS diagnosis.
“In contrast, ensemble classification, combining multiple models, demonstrated improved accuracy and robustness [21,22].”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [21]
Section 4.1 (page unavailable) reports improved sensitivity and specificity from combining a radiologist’s and a computer system’s assessments of breast lesions. It does not address an ensemble of multiple models or improved accuracy and robustness for PAS diagnosis; searches for ensemble classification and placenta accreta found no supporting passage. (LLM, conf=0.75)
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References 144 · Cited by 262
Research works printed but not linked: 1
- [33]Mitchell TM Machine learning WCB McGraw-Hill Boston, MA 1997
- Authors
- Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E.
- Affiliations
- MIT Lincoln Laboratory (US)
- Carney Hospital (US)
- University of Florida Health (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Brattain, Laura J.
- Venue
- Abdominal Radiology
- Published
- 2018-02-28
- Volume
- 43
- Issue
- 4
- Pages
- 786-799
- ISSN
- 2366-004X (ISSN-L)
- 2366-0058
- PMC
- PMC5886811
- OpenAlex
- W2792252898
- Cited by
- 262
- References
- 144
- Citations by year
- 2026: 18 · 2025: 38 · 2024: 41 · 2023: 47 · 2022: 38 · 2021: 36 · 2020: 24 · 2019: 13 · 2018: 6
- FWCI
- 5.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Abdominal Radiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (HHSN268201300071 C, K23 EB020710)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- ultrasonography, machine learning, ultrasound image processing, inter-operator variability, intra-operator variability, image quality control, point-of-care ultrasound, clinical workflow, disease diagnosis, disease characterization
- MeSH terms
- Abdomen, Machine Learning, Forecasting, Humans, Ultrasonography
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