Acta cytologica · 2012
Computer-assisted diagnosis in colposcopy: results of a preliminary experiment?
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mehlhorn Grit, Münzenmayer Christian, Benz Michaela, Kage Andreas, Beckmann Matthias W, Wittenberg Thomas
Аннотация
PURPOSE: Diagnosis of cervical intraepithelial neoplasia (CIN) is currently based on the histological result of an aiming biopsy. This preliminary study investigated whether diagnostics for CIN can potentially be improved using semiautomatic colposcopic image analysis.
METHODS: 198 women with unremarkable or abnormal smears underwent colposcopy examinations. 375 regions of interest (ROIs) were manually marked on digital screen shots of the streaming documentation, which we provided during our colposcopic examinations (39 normal findings, 41 CIN I, and 118 CIN II-III). These ROIs were classified into two groups (211 regions with normal findings and CIN I, and 164 regions with CIN II-III). We developed a prototypical computer-assisted diagnostic (CAD) device based on image-processing methods to automatically characterize the color, texture, and granulation of the ROIs.
RESULTS: Using n-fold cross-validation, the CAD system achieved a maximum diagnostic accuracy of 80% (sensitivity 85% and specificity 75%) corresponding to a correct assignment of abnormal or unremarkable findings.
CONCLUSIONS: The CAD system may be able to play a supportive role in the further diagnosis of CIN, potentially paving the way for new and enhanced developments in colposcopy-based diagnosis.
METHODS: 198 women with unremarkable or abnormal smears underwent colposcopy examinations. 375 regions of interest (ROIs) were manually marked on digital screen shots of the streaming documentation, which we provided during our colposcopic examinations (39 normal findings, 41 CIN I, and 118 CIN II-III). These ROIs were classified into two groups (211 regions with normal findings and CIN I, and 164 regions with CIN II-III). We developed a prototypical computer-assisted diagnostic (CAD) device based on image-processing methods to automatically characterize the color, texture, and granulation of the ROIs.
RESULTS: Using n-fold cross-validation, the CAD system achieved a maximum diagnostic accuracy of 80% (sensitivity 85% and specificity 75%) corresponding to a correct assignment of abnormal or unremarkable findings.
CONCLUSIONS: The CAD system may be able to play a supportive role in the further diagnosis of CIN, potentially paving the way for new and enhanced developments in colposcopy-based diagnosis.