ArticleJournal of global oncology2018
Palpable Breast Lump Triage by Minimally Trained Operators in Mexico Using Computer-Assisted Diagnosis and Low-Cost Ultrasound.
Article in Journal of global oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 35 citations in OpenAlex.
- Review
- Review
- Clinical Application of Artificial Intelligence in Breast Ultrasound.Journal of the Korean Society of Radiology · 2025Review
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- AI education for clinicians.EClinicalMedicine · 2025Review
- Breast cancer classification in point-of-care ultrasound imaging-the impact of training data.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Accuracy of breast ultrasound image analysis software in feature analysis: a comparative study with sonographers.Scientific reports · 2024Article
- From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings.PLOS digital health · 2024Article
- Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.Archives of public health = Archives belges de sante publique · 2024Article
- Artificial intelligence in breast ultrasound: application in clinical practice.Ultrasonography (Seoul, Korea) · 2024Article
- ChatGPT: promise and challenges for deployment in low- and middle-income countries.The Lancet regional health. Western Pacific · 2023Review
- Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting.Radiology · 2023Article
- Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions.Cancers · 2023Review
- AI: Can It Make a Difference to the Predictive Value of Ultrasound Breast Biopsy?Diagnostics (Basel, Switzerland) · 2023Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review.NPJ digital medicine · 2022Article
- Review
- Technical note: Low clinical efficacy, but good acceptability of a point-of-care electronic palpation device for breast cancer screening for a lower middle-income environment.Medical physics · 2022Article
- High-burden Cancers in Middle-income Countries: A Review of Prevention and Early Detection Strategies Targeting At-risk Populations.Cancer prevention research (Philadelphia, Pa.) · 2021Review
- Point-of-Care Ultrasound: Applications in Low- and Middle-Income Countries.Current anesthesiology reports · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Purpose In low- to middle-income countries (LMICs), most breast cancers present as palpable lumps; however, most palpable lumps are benign. We have developed artificial intelligence-based computer-assisted diagnosis (CADx) for an existing low-cost portable ultrasound system to triage which lumps need further evaluation and which are clearly benign. This pilot study was conducted to demonstrate that this approach can be successfully used by minimally trained health care workers in an LMIC country. Patients and Methods We recruited and trained three nonradiologist health care workers to participate in an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant pilot study in Jalisco, Mexico, to determine whether they could use portable ultrasound (GE Vscan Dual Probe) to acquire images of palpable breast lumps of adequate quality for accurate computer analysis. Images from 32 women with 32 breast masses were then analyzed with a triage-CADx system, generating an output of benign or suspicious (biopsy recommended). Triage-CADx outputs were compared with radiologist readings. Results The nonradiologists were able to acquire adequate images. Triage by the CADx software was as accurate as assessment by specialist radiologists, with two (100%) of two cancers considered suspicious and 30 (100%) of 30 benign lesions classified as benign. Conclusion A portable ultrasound system with CADx software can be successfully used by first-level health care workers to triage palpable breast lumps. These results open up the possibility of implementing practical, cost-effective triage of palpable breast lumps, ensuring that scarce resources can be dedicated to suspicious lesions requiring further workup.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.