An update on intraoral application of colposcopy

Diagnostic accuracy of colposcopic examination in patients with oral dysplastic lesions

OBJECTIVE

Colposcopy is a direct microscopic method and is the gold standard tool to detect early cervical dysplastic lesions. In the past, many attempts have been made to use gynecologic methods to examine the oral mucosa. The aim of this study was to detect the diagnostic accuracy of oral colposcopy in diagnosing oral dysplastic lesions in comparison with microscopic evaluation based on biopsy and compare Reid's Colposcopic Index (RCI) and the Swede scoring system in diagnosing oral dysplastic lesions.

STUDY DESIGN

Twenty-five patients who presented for diagnosis of oral leukoplakia to the Oral Medicine and Periodontology Department, Faculty of Dentistry, Cairo University (Cairo, Egypt) and met the selection criteria were recruited in this study. Each patient was subjected to colposcopic examination, followed by biopsy to confirm the results of colposcopy. The sensitivity and specificity of oral colposcopy were calculated after colposcopic assessment by using the Swede scoring system and the RCI.

RESULTS

The diagnostic accuracy of oral colposcopy with use of the Swede scoring system was superior to that of oral colposcopy with the use of the RCI.

CONCLUSIONS

Colposcopic examination using the Swede scoring system is very specific for diagnosing oral epithelial dysplasia and for using the "see-and-treat" method, whereas the RCI is a very sensitive screening method for the diagnosis of oral epithelial dysplasia in oral potentially malignant disorders.

High resolution multispectral endoscopy significantly improves the diagnostic accuracy of cervical intraepithelial lesions

Colposcopy is a visual technique to examine the cervix and determine selection of sites for biopsies and eligibility for treatment. It's always been a critical part of identifying preinvasive and early invasive cervical carcinoma. Unfortunately, challenges exist with regards to the accuracy of traditional colposcopy. Hence, to fully exploit the benefit of increasing diagnostic sensitivity, there is a pressing need to improve the performance of colposcopy by applying novel innovations and techniques. In this case report, we used a recently developed, high-resolution multispectral endoscopy and evaluated its performance by comparing colposcopic image features (the vascular pattern, in particular, depending upon the improved optics and illumination) with histology results. High-resolution multispectral endoscopy makes it easier to distinguish the features of pathological vessels, so that it has a higher sensitivity and specificity to detect cervical lesions, especially in discriminating the vascular pattern using multispectral technology.

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

BACKGROUND

Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations.

OBJECTIVE

In this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians' interpretation.

METHODS

A smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA.

RESULTS

An average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA.

CONCLUSIONS

We explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.