Artificial Intelligence in Cervical Cancer

Artificial Intelligence in Cervical Cancer

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  AI IN CANCERCERVIX GLOBAL CONFERENCE ON UPDATES IN OBSTETRICS & GYNECOLOGY (GCUO - 2026) HYDERABAD 8th AUGUST 2026
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  Name: DR. NIRANJANN. CHAVAN Designation: PROFESSOR & HEAD OF UNIT Treasurer, FOGSI ((2025-2027), Vice Chair, Maharashtra Chapter of IAGE (2025-27) Organising Secretary, AICOG Mumbai 2025 President, MOGS (2022-2023) President Elect, AFG (2026-2027) Member Oncology Committee, SAFOG (2021-2027) AOFOG (2013-2015) Member, Managing Committee IAGE (2013-17), (2018-20), (2022-2023) Dean AGOG & Chief Content Director, HIGHGRAD & FEMAS Courses Editor-in-Chief, FEMAS, JGOG & TOA Journal 124 publications in International and National Journals with 295 Citations Editorial Board, European Journal of Gynaec. Oncology (Italy) Affiliation: LTMMC & SION HOSPITAL Place: SION, Mumbai, India.
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  WHAT IS AI? •Artificial intelligence (AI) is a type of digital computer system that parallels the way the human brain processes information. • AI is organized in a similar way that neurons in the brain are arranged, with their multiple neural nodes, and so are referred to as neural networks. • The rise of AI has led to the subsequent development of artificial neural networks (ANN), which consist of a dependable mathematical system that can interpret multifactorial data.
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  WHAT IS AI? • These neurons are connected via multiple synapses and send the data to each other back and forth, and by doing so, come up with the most probable answer. • Making these multiple connections enables computers to mimic cognitive functions, such as the reasoning process, problem solving and pattern recognition to identify the most probable answer to a problem.
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  • This complexalgorithm AI software is now utilized in medicine to analyze large amounts of data, which can assist in disease prevention, diagnosing, and monitoring patients. • Overall, AI can aid practitioners in decision-making and will help clinicians to make more self-assured decisions.
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  WHAT IS MACHINELEARNING ? • ML, is a form of AI, in which a machine can learn and adapt to situations and undergo self-driven data training. • Typically, a training data set is used to train a computer program by feeding images describing a series of features such as colour, shape, and texture. • Two main approaches to ML, viz supervised and unsupervised learning.
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  ARTIFICIAL NEURAL NETWORKS • Aneural network typically consists of several layers of artificial neurons, fully connected to each other. • Each neuron receives signals from multiple neurons from the previous layer, integrates these signals, and then fires these integrated signals, in all directions. • ANNs are mathematical systems which are reliable, flexible and evaluate multifactorial data at lightening speed.
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  AI IN OBGYN •Fetal Heart Rate Monitoring and Pregnancy Surveillance • GDM • Preterm Labour & AI in Ultrasound • IVF • Urogynecology • Gynae. Oncology • Parturition
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  AI IN GYNAECOLOGIC ONCOLOGY
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  • This technologycan automatically extract image patches coarsely centred on the nucleus as network input, which means that it can extract deep features embedded in cell image blocks for classification. AI IN CERVICAL CANCER
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  • It wasfound that this method yielded the highest performance not only on the Herlev Pap smear, but also on the H&E staining manual liquid-based cytology (HEMLBC) liquid-based cytology datasets. • Therefore, it is expected that this type of cervical cell classification system with segmentation-free and high accuracy will be developed into an automatic assisted reading system for primary cervical screening.
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  • The currentscreening consists of visual inspection of the specimen collected during a Papanicolaou (PAP) smear and using acetic acid to visualize whitening in the tissue which would be indicative of disease. • Despite its convenience and low cost, it lacks accuracy. • AI has outperformed human experts in interpreting cervical pre-cancer images.
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  Computer Methods andPrograms in Biomedicine 138 (2017) 31–47 • Classified Pap smear images in their research by using the integrated classifier which was designed with three popular individual classifiers: SVM, Neural network Multilayer perceptron (MLP) and Random forest classifier (RF). • All features have proved that it is very important for the classification of Pap smear samples, and a single feature cannot provide high accuracy in the classification process. • The study also found that the performance of the ensemble classifier is the best, and the performance of MLP and SVM are similar, both of which are better than RF.
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  • For noncancerousand cancerous cervical images, the proposed system achieved classification accuracy of 97.14% and 100%, respectively • This proposed methodology for cervical image classification achieved 98.57% of the total classification accuracy Asian Pac J Cancer Prev. 2018;19(11):3203–3209
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  • The performanceanalysis of the proposed cervical cancer detection and segmentation system showed that its sensitivity was 97.42%, specificity was 99.36%, and segmentation accuracy was 99.36%. • Therefore, the simulation on these cervical image data sets shows that the new method is superior to the traditional cervical cancer detection and segmentation methods and has higher performance in clinical practice
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  CerviCARE AI byNTL Healthcare is an award-winning, on-device artificial intelligence system for cervical cancer screening. It analyzes cervicography images in about five seconds without requiring internet connectivity, delivering 98% sensitivity and 95.5% specificity for detecting high- grade precancerous lesions. . 2024 Jan 23;14:1957. doi: 10.1038/s41598-024-51880-4
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  Core Features &Workflow: •On-Device Analysis: Runs locally on handheld hardware without cloud or internet dependence. •Rapid Results: Generates risk classification and morphological insights within five seconds of image capture. •Image Quality Guide: Built-in software assists operators in capturing optimal clinical photographs of the cervix. •Low-Resource Adaptability: Designed for mobile clinics, remote regions, and areas lacking resident gynecologic specialists.
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  • The studyidentified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. • The machine learning/deep learning (DL) algorithms applied showed the best diagnostic performances, with an accuracy of over 97%. • The study concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising.
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  • CerviXpert, amulti-structural convolutional neural network model designed to efficiently classify cervix types and detect cervical cell abnormalities. • CerviXpert achieved an accuracy of 98.04 percent in classifying cervical cell abnormalities into three classes and 98.60 percent for five class cervix type classification. . 2025 Jan 22;18:30. doi: 10.1186/s13104-025-07086-6
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  NEW ADVANCES OF AIIN CERVICAL CANCER • Automated Visual Evaluation (AVE): deep learning applied directly to cervical images for point-of-care screening of precancerous lesions. • Multi-country validation studies assessing how well AI models generalise across camera and smartphone images. • AI-assisted colposcopy and digital cytology for CIN grading and detection of precancerous lesions. • Combined AI + HPV testing pathways for screening and triage in low-resource settings.
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  AUTOMATED VISUAL EVALUATION (AVE) •NCI-developed deep learning model (Faster R- CNN) trained on cervical images to detect precancerous lesions. • AVE achieved an AUC of 0.91, compared with 0.69 for human expert review and 0.71 for conventional cytology. • A multi-country NCI study evaluated AVE performance across six countries using both camera and smartphone images, highlighting the importance of data diversity in AI model design.
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  • In thisstudy Automated visual evaluation(AVE) of enrollment cervigrams identified cumulative precancer/cancer cases with greater accuracy than original cervigram interpretation or conventional cytology. • A single visual screening round restricted to women at the prime screening ages of 25-49 years could identify 127 (55.7%) of 228 precancerous lesions diagnosed cumulatively in the entire adult population. J Natl Cancer Inst. 2019 Jan 10;111(9):923–932. doi: 10.1093/jnci/djy225
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  AI-ASSISTED COLPOSCOPY & DIGITALCYTOLOGY • Deep learning models now support precision grading of cervical cytology and colposcopic images, aiding CIN classification. • Whole-slide image analysis using deep learning enables robust, automated cervical screening from digitised slides. • Multispectral imaging combined with AI is being explored for real-time detection of premalignant cervical lesions.
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  • CITOBOT v4is a portable medical device designed for cervical cancer screening in low-resource settings, combining an ergonomic opening mechanism, a built-in megapixel USB camera, and artificial intelligence software for offline risk assessment. • It guides healthcare providers through cervical cancer screening, transmitting cervical images to the AI system, which responds within seconds. The software can operate offline, allowing seamless use in areas with limited connectivity. It also includes features for tracking patient screening history. Computational and Structural Biotechnology journal, Volume 24, December 2024
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  • The transvaginalimaging probe (GynoSight) was used to perform real-time detection of region of interest and AI model incorporated in it was implemented to identify the atypical blood vessels, dense acetic acid uptake and negative uptake of Lugol’s iodine. • The model achieved an accuracy of 86.67% for the identification of Iodine negative regions European Journal Of Obstetrics And Gynecology and Reproductive Biology, Volume313, September 2025
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  ONGOING TRIALS &GLOBAL INITIATIVES • Prospective validation trials (e.g. an AVE / CINFinder study in El Salvador, ~10,000 women) are comparing AI-based triage with HPV testing and visual inspection with acetic acid (VIA). • AI-based screening tools are being positioned to support the WHO's global strategy for cervical cancer elimination, particularly in low- and middle- income countries. • Open datasets and collaborative research networks are accelerating model development and external validation.
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  • Cerviray Alis a portable colposcopy system developed by AIDOT in South Korea that uses artificial intelligence to detect pre-cancerous cervical lesions. • The device combines a handheld colposcope with Al software to classify cervical images as normal, CIN1, CIN2/3, or CIN3+, delivering results within seconds. It is designed for use by untrained operators in areas with limited access to specialists. • It has reported 93% sensitivity and 89% specificity in initial studies.
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  • The Alsystem is built on a deep learning model trained with over 10,000 to 30,000 colposcopic images and histopathological diagnoses from expert gynecologists. • It includes three modules: • A satisfactory filtering module to check image quality. • A preprocessing module to adjust contrast and brightness. • A classification module for lesion diagnosis.
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  CASUAL CERVIX NET •Convolutional neural networks with casual insight (CICNN) in cervical cancer cell classification. • This study introduces CasualCervixNet, an advanced deep learning framework that incorporates casual inference techniques for more accurate classification of cytology images. • By leveraging a structured approach to casual reasoning, CasualCervixNet transcends the limitations of conventional ML models offering enhanced interpretability and diagnostic precision.
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  APPLICATION OF COLPOSCOPIC IMAGESAND AI IN THE DIAGNOSIS OF CERVICAL PATHOLOGY - A CLINICAL STUDY • The study (2025-27) is being conducted at the Department of Obstetrics & Gynaecology, LTMGH, Sion, Mumbai, under guidance of Dr. Niranjan Chavan. • It evaluates the role of AI in diagnosing cervical pathologies using colposcopic images. • Compares AI-assisted diagnosis with colposcopist interpretation using histopathology as the gold standard. • It will help assess the potential of AI to improve diagnostic accuracy and support cervical cancer screening.
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  • Approximately 150colposcopic images have been collected in Gynacology OPD and organized for analysis. The images have been annotated and preprocessed for AI model training and validation. • Preliminary model training has been initiated using these images to evaluate diagnostic performance in identifying cervical lesions. • The project is currently focused on optimizing the model and comparing its results with standard colposcopic diagnosis.
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  TAKE HOME MESSAGE •AI has a promising future in overcoming diagnostic challenges and improving treatment modalities and patient outcomes in OBGYN • Further studies need to be done to decrease bias when creating algorithms and to increase adaptability in the system, enabling the incorporation of new medical knowledge as new technology surfaces • AI is not meant to replace practitioners but rather to serve as an adjunct in decision-making
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  TAKE HOME MESSAGE •Clinicians must embrace them, yet be wary, and when necessary, recognize its advantages and drawbacks to continue providing the best patient care. • AI is properly used and its applications in clinical practice are optimized,67 it will be regarded as a valuable tool. • AI can not only be used as a promising tool in gynecologic malignant tumors, but also as a method to resolve several long-term challenges.
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  THANK YOU!