The detection of cervical neoplasia via optical imaging: a pilot clinical study

Purpose The present study aims to develop a new high-resolution imaging system for the early diagnosis of cervical neoplasia based on increased vessel density of the cervical tissue. Methods An optical device was developed to obtain high contrast and resolution images of vascular structures of the cervix in the present study. The device utilizes a telecentric lens to capture cervix images under light illumination with a wavelength of 550 nm emitted from LEDs. Images were obtained using the telecentric lens with or without acetic acid application to the cervix. Image processing algorithms were used to contrast and extract the skeleton of the vascular structures on the cervix. In the evaluation of the vascular density, the cervical images were divided into 12 o'clock positions, and the fractal dimension of the vascularity was calculated for each dial area between the o'clock positions. The region with the largest fractal dimension was accepted as the region with the highest probability of lesion. The range of vessel sizes was split into small classes of bins for each dial area with the highest fractal dimension. To validate the system's success in differentiating between normal and HSIL lesions, forty five patients who underwent colposcopy and biopsy were included in a pilot study. Results The system correctly classified four HSIL cases out of five and failed to detect one HSIL case, achieving an accuracy rate of 97.8% with an 80% sensitivity and 100% specificity. Conclusion The developed high-resolution optical imaging system may potentially be used in detecting cervical neoplasia just before the biopsy and reduce the number of false-positive cases.

Automated summary

A new high-resolution imaging system based on increased vessel density of cervical tissue was developed for the early diagnosis of cervical neoplasia. A pilot study with 45 patients who underwent colposcopy and biopsy showed that the system achieved a high accuracy rate of 97.8% with 80% sensitivity and 100% specificity in differentiating between normal and HSIL lesions.