Automating the Detection of Acetowhite Lesions by Classifying the Temporal Behavior of Cervical Regions
The proposed method was effective in mimicking expert colposcopists in identifying lesions based on classification of time-series images and outputs correlated with histology. Application of a machine learning model in the clinical setting may streamline lesion identification, support clinician decision-making for punch biopsy, and eventually improve accuracy in detecting precancerous lesions.
Authors
- Jayashree Kalpathy-Cramer
- Emmanouil Papagiannakis (ORCID: https://orcid.org/0000-0001-8867-9203)
- André Aquilina
- Christopher Clark (ORCID: https://orcid.org/0009-0007-2558-7396)
- Rebecca B. Perkins (ORCID: https://orcid.org/0000-0002-7054-3014)
Institutions
- Tufts University (US)
- Tufts Medical Center (US)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- University of Colorado Anschutz Medical Campus (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- Journal of Lower Genital Tract Disease
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1097/lgt.0000000000000927
- Citations
- 1
- Primary Topic
- Cervical Cancer and HPV Research
- Type
- article
- Field-Weighted Citation Impact
- 1.35