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.
Thus, the system could be able to support practitioners with less experience or in private practice. In combination with a connected case database it can also support case-based reasoning for the diagnostic decision process.
Abstract. Due to the actual demographic development the use of Computer-Assisted Diagnosis (CAD) systems becomes a more important part of clinical workflows and clinical decision making. Because changes on the mucosa of the esophagus can indicate the first stage of cancerous developments, there is a large interest to detect and correctly diagnose any such lesion. We present a knowledge-based system which is able to support a physician with the interpretation and diagnosis of endoscopic images of the esophagus. Our system is designed to support the physician directly during the examination of the patient, thus prodving diagnostic assistence at the point of care (POC). Based on an interactively marked region in an endoscopic image of interest, the system provides a diagnostic suggestion, based on an annotated reference image database. Furthermore, using relevant feedback mechanisms, the results can be enhanced interactively.
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