Clinical trial · Interventional
A Trial on the Use of Point-of-care Ultrasound in the Assessment of Breast Symptoms
Breast Point-of-Care Ultrasound Examination Trial
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The high cost of diagnostic equipment, limited expertise, and inadequate infrastructure are major barriers to early breast cancer diagnosis in low- and middle-income countries. Point-of-care ultrasound (POCUS) offers a relatively low-cost, portable solution that, when combined with artificial intelligence (AI)-driven image analysis, has the potential to significantly expand access to breast assessment in these settings. The purpose of this study is to evaluate the performance of POCUS for women with focal breast symptoms and to assess the performance of AI to analyze POCUS images. The study will be divided in two parts: a prospective interventional study and a retrospective multicase multireader study.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Point-of-care ultrasound | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Intervention
- interventionNames
- Diagnostic Test: Point-of-care ultrasound
Primary outcomes (1)
- measure
- The area under the receiver operating characteristic curve (AUC) for the intervention, compared to that of the comparator
- timeFrame
- From the last enrolled participant to the end of one-year follow up
- description
- 1. The AUC of POCUS compared to SoC 2. The AUC of AI compared to average radiologists on POCUS
Secondary outcomes (1)
- measure
- The performance of POCUS and POCUS AI
- timeFrame
- From the last enrolled participants to the end of one year follow up
- description
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Women (≥18 years of age) referred to diagnostic imaging with a suspicion on malignancy Exclusion Criteria: * Individuals unable to comprehend the study information due to language barriers or cognitive impairments.
References
Publications (3)
- BACKGROUNDKarlsson J, Arvidsson I, Sahlin F, Astrom K, Overgaard NC, Lang K, Heyden A. Breast cancer classification in point-of-care ultrasound imaging-the impact of training data. J Med Imaging (Bellingham). 2025 Jan;12(1):014502. doi: 10.1117/1.JMI.12.1.014502. Epub 2025 Jan 17. PMID 39830074
- BACKGROUNDKarlsson, J, Wodrich, M, Overgaard, NC, Sahlin, F, Lång, K, Heyden, A & Arvidsson, I 2025, Towards Out-of-Distribution Detection for Breast Cancer Classification in Point-of-Care Ultrasound Imaging. in, Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings, Part XIII. Lecture Notes in Computer Science
- BACKGROUNDWodrich, M, Karlsson, J, Lång, K & Arvidsson, I 2025, Trustworthiness for Deep Learning Based Breast Cancer Detection Using Point-of-Care Ultrasound Imaging in Low-Resource Settings. in Medical Information Computing: MICCAI Meets Africa Workshop, https://doi.org/10.1007/978-3-031-79103-1_5.