Cervical cytology has been widely used for cervical cancer screening but global implementation poses many challenges and the suboptimal sensitivity of the test requires repeat screening. The World Health Organization has recommended HPV-based tests for initial screening due to the high sensitivity and objective nature of the test.
While cytology of HPV-infected women for colposcopy prior to confirmation of abnormal cervical cytology is an effective primary approach in high-income countries, dissemination of this strategy requires well-organized infrastructure and the expertise of professionals, including pathologists. , cytopathologists, and laboratory scientists. , and experienced colposcopy specialists.
The World Health Organization also recommends visual inspection with acetic acid (VIA) for triage of HPV-infected women because VIA is a cost-effective approach suitable for resource-limited settings. However, VIA has a strong diagnostic character and may lack specificity, which may result in pre-cancerous lesions going undetected for a long period of time. Colposcopy, as a diagnostic tool, also has the limitations of subjective testing that requires a high level of competence.
Recent advances in artificial intelligence (AI) offer great prospects for automated, objective and unbiased detection of cervical cancer and precancerous conditions. The idea of computers that mimic actual human behavior, perception, and thinking was proposed by Alan Turing as early as 1950. The term "artificial intelligence" was formally coined by John McCarthy at an academic conference in 1956.
Artificial intelligence began with the following major trends: sensory perception; Bayesian networks; pattern recognition; Human-computer interaction; knowledge representation; And computer vision. As AI entered the “golden” age, there was a rise in interest and AI performance gradually evolved into complex algorithms resembling human logic.
As machine learning develops as a core AI technology, computers can learn from data analysis, extract criteria from the data, and use these criteria to predict and classify unknowns. Computers have the ability to automatically find, learn, and recognize new text, images, signals, and other data
From the development of artificial neural networks, the concept of deep learning emerged and is now widely used in the fields of medical diagnosis, medical image recognition, natural language processing, and health management applications.
In recent years, artificial intelligence has shown significant advantages in many aspects of cervical cancer detection, including cell segmentation and classification, colposcopy, and early detection of lymph node metastasis (LNM) of cervical cancer by MRI ( MRI)
A large proportion of current research focuses on developing deep learning algorithms for automatic processing, recognition, feature extraction, and classification of cervical images, enabling artificial intelligence to analyze images, identify patterns, and interpret cancer characteristics. The World Health Organization notes that artificial intelligence can enhance screening tests and technologies that include visual evaluation of digital images.
