Investigator
Jawaharlal Institute Of Post Graduate Medical Education And Research
Development and clinical validation of a clinically translatable non-chip-on-tip transvaginal imaging system (GynoSight v2.0) for early detection of premalignant cervical lesions
Cervical cancer is the fourth most common cancer among women globally. Hence, it is crucial to develop a noninvasive and portable optical imaging modality for the early detection of premalignant cervical lesions. We present the development and clinical validation of GynoSight v2.0, an indigenously developed multispectral, non-chip-on-tip source, hand-held, portable transvaginal imaging probe, for evaluating tissue health and identifying anomalies, such as those linked to precancerous cervical lesions. GynoSight v2.0 houses a 16 LEDs, 5-megapixel camera, and a Raspberry Pi 5 module. A comparative shadowing effect analysis was performed between GynoSight v2.0 and colposcopy by evaluating statistical metrics such as mean pixel intensity (MPI), shadow area percentage (SAP), entropy, and contrast-to-noise ratio. In addition, the relative oxygen saturation maps of the cervical tissue were computed from the multispectral registered image using the proposed discrete Fourier transform-based image registration technique. The images of The colposcopy images showed more shadowing effects than the GynoSight v2.0 images and hence provide better illumination to aid in better diagnosis.
Real-time detection of premalignant cervical lesion using Artificial Intelligence (AI) model in multispectral imaging system
Cervical cancer is the fourth most common cancer among women globally. The early diagnosis of cervical cancer can significantly improve prognosis and effective prevention. The accuracy of the cervical examination varies due to inter-clinician variability. This work is aimed to develop a real-time and static AI model integrated in Multispectral imaging System (GynoSight) with Graphical User Interface to assist the clinician in detecting abnormal blood vessels, acetowhite uptake, and iodine negative regions during the cervix screening. In this study, the dataset is acquired from the Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry between February 2024 to December 2024. The colposcopy images were acquired from the subjects between the age group of 22-65 years after smearing normal saline, 3 % acetic acid, and Lugol's Iodine. The preprocessing steps are applied on all images and the dataset is framed with 609 images, 196 normal saline-smeared cervix images,212 acetic acid-smeared cervix images, and 201 Lugol's iodine-smeared cervix images which were used to train and validate the model. The EfficientNet architecture AI model was implemented to identify the atypical blood vessel, dense acetic acid uptake and negative uptake of Lugol's iodine. The transvaginal imaging probe (GynoSight) was used to perform real-time detection of region of interest using a trained TensorFlow model. The proposed model achieved an accuracy of 86.67% for the identification of Iodine negative regions and an F1 score of 90% and mAP (50%) of 91.1%. The updated proposed model implemented for the detection of the acetic acid uptake was reported with an improvement of accuracy from 57% to 85.71% after applying segmentation algorithm which indicates the reliability and robustness of the system. Furthermore, the detection of atypical blood vessels, acetowhite region, and Iodine negative uptake regions by the AI system is compared with clinician interpretation followed by the biopsy results. The AI-assisted real-time detection system may assist the clinician during screening, which will reduce the number of unnecessary biopsies for the patients, reducing the inter-clinician variability.
Optical imaging for early detection of cervical cancer: state of the art and perspectives
Cervical cancer is one of the major causes of death in females worldwide. HPV infection is the key cause of uncontrolled cell growth leading to cervical cancer. About 90% of cervical cancer is preventable because of the slow progression of the disease, giving a window of about 10 years for the precancerous lesion to be recognized and treated. The present challenges for cervical cancer diagnosis are interobserver variation in clinicians' interpretation of visual inspection with acetic acid/visual inspection with Lugol's iodine, cost of cytology-based screening, and lack of skilled clinicians. The optical modalities can assist in qualitatively and quantitatively analyzing the tissue to differentiate between cancerous and surrounding normal tissues. This work is on the recent advances in optical techniques for cervical cancer diagnosis, which promise to overcome the above-listed challenges faced by present screening techniques. The optical modalities provide substantial measurable information in addition to the conventional colposcopy and Pap smear test to clinically aid the diagnosis. Recent optical modalities on fluorescence, multispectral imaging, polarization-sensitive imaging, microendoscopy, Raman spectroscopy, especially with the portable design and assisted by artificial intelligence, have a significant scope in the diagnosis of premalignant cervical cancer in future.
