Diagnostic test of bioimpedance-based neural network algorithm in early cervical cancer.

ABSTRACT:

Background

Colposcopy is a critical component of cervical cancer screening services, but the accuracy of colposcopy varies greatly due to the lack of standardized training for colposcopists and pathologists. Thus, to improve the accuracy of colposcopy in the detection of cervical lesions intelligently is urgent. Here, we explored the sensitivity and specificity of a bioimpedance-based neural network algorithm in distinguishing normal and precancerous cervical tissues.

Methods

Bioimpedance data were collected using a bioimpedance analyzer (Mscan1.0B, Sealand Technology, Chengdu, China) from the cervices of 102 female patients with abnormal cervical cytology (≥atypical squamous cells of undetermined significance) who required further colposcopy. Finally, the data of 106 sample

SUBMITTER: Mei L 

PROVIDER: S-EPMC9096428 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature