Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.
Jue WangYunfang YuYujie TanHuan WanNafen ZhengZifan HeLuhui MaoWei RenKai ChenZhen LinGui HeYongjian ChenRuichao ChenHui XuKai LiuQinyue YaoSha FuYang SongQingyu ChenLina ZuoLiya WeiJin WangNengtai OuyangHe-Rui YaoPublished in: Nature communications (2024)
Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.
Keyphrases
- artificial intelligence
- cervical cancer screening
- phase iii
- big data
- phase ii
- fine needle aspiration
- deep learning
- global health
- open label
- machine learning
- high grade
- double blind
- clinical trial
- placebo controlled
- study protocol
- ultrasound guided
- cross sectional
- induced apoptosis
- public health
- rna seq
- resistance training
- electronic health record
- randomized controlled trial
- cell cycle arrest
- endoplasmic reticulum stress
- body composition
- pi k akt