Gynecological Cancer Screening:
Neural Network models are being used to deliver prognoses in patients with ovarian cancer. Ovarian cancer is a catchall for heterogeneous neoplasm, and there is a great variation in histology and inpatient presentation such as existing tumor stages. In a report done by Enshaei et al. their results demonstrated that ANN was able to predict survival with a 97% accuracy [23]. The AI systems they developed have the potential of providing an accurate prognosis. Similarly, Norwitz et al. have created an AI software that can predict prognosis in patients with ovarian cancer, more precisely than current methods [18]. It can also predict the most effective treatment according to the diagnosis of each patient. Long term survival rates for advanced ovarian cancer are poor, thus more targeted therapies are needed.
Researchers at Brigham and Women’s Hospital and Dana-Farber Cancer Institute have been using AI to manipulate large amounts of micro RNA data to develop models that can potentially diagnose early ovarian cancer [23]. Currently, no screening for ovarian cancer exists despite it being a common gynecological cancer. Thus, most cases are diagnosed in advanced stages leading to a high five-year mortality rate. The AI neural network could keep up with the complex interactions between micro RNA and accurately identified almost 100% of abnormalities that represented ovarian cancer; as opposed to an ultrasound screening test that was able to identify abnormal results less than 5% of the time. This non-invasive testing consists of measuring micro RNAs from a serum sample, which can be paramount for the future management of ovarian cancer.
To identify patients at risk of more aggressive tumors, a newly developed AI system has been created to scan ovarian cancer cells; this system can help identify irregularly shaped nuclei that correlate with tumor aggressiveness [19,23] Scanning by an AI system can be incorporated in routine biopsies to identify these risk factors related to DNA instability and chose therapies accordingly. Evasion of the immune system has been identified in the misshapen nuclei in aggressive ovarian cells, indicating that there can be a response to immune targeted treatments such as onco-immunotherapy.
AI has recently been incorporated into oncology through commercial applications that use the algorithm to match patient data with current clinical trials nationwide and respective investigational drugs per patient [24,25,26]. Watson for Oncology uses AI in conjunction with patient data to help guide cancer management, which has proven efficient for breast cancer patients [27].
Furthermore, AI has outperformed human experts in interpreting cervical pre-cancer images [26]. The current screening consists of visual inspection of the specimen collected during 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 deep learning algorithms can gather a large number of images related to cervical cancer screening and appropriately identify diseased tissue. The use of automatic visual evaluation can be utilized in everyday devices such as the camera device of the cell phone. Thus, ensuring that the test is convenient and of low cost unlike the current method; in addition to that, accuracy is improved, minimal training is required, and results are immediate, thus patients can receive treatment in the same visit [24,25,26].