aceAn explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers
Background Cervical cancer screening in primary care is hindered by expert pathologist shortages and heavy diagnostic workloads, leading to fatigue-induced misdiagnoses. This study evaluated the diagnostic capacity, subpopulation robustness, and operational efficiency of an interpretable machine learning (ML) tool within a large Chinese healthcare network. Methods A retrospective database of 5,000 women was audited. Archived liquid-based cytology (LBC) digital slides were evaluated via a parallel validation chan…
In a retrospective audit of 5,000 women, the LBC-NET pipeline detected HSIL+ with higher sensitivity than senior cytopathologists and reduced review time while increasing daily capacity.
Evidence
- Peer-reviewedActa Clinica Belgica2026-07-29
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Truvace Impact Record TRV-2026-0588, v1: “An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers.” Truvace, 2026-07-30. /record/TRV-2026-0588 (accessed at citation time). sha256 b5e83e1448af78bd…
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