E5330. Artificial Intelligence in Gynecological Imaging
Authors
G. S. Triveni;
Lady Hardinge Medical College
Chandrashekhara Sheraganu-Hanumanthappa;
All India Institute of Medical Sciences
Background
Artificial intelligence (AI) has the potential to enhance current imaging techniques in several ways. AI can play a significant role in gynecological imaging, improving efficiency of diagnosis and preventing errors, ultimately patient outcomes. Our exhibit highlights the advantages, limits, challenges, and ethical issues, and provides insight into future prospects of AI in gynecological imaging.
Educational Goals / Teaching Points
AI can be used in gynecological imaging for a variety of applications such as image segmentation, classification, and prediction. In image segmentation, AI algorithms can automatically identify and segment different anatomical structures, e.g., tumors. This can facilitate the diagnosis of gynecological tumors by accurate volumetry of tumors, which can be used for follow-up and in demarcating the surgical margin preoperatively. AI can also be used to classify images into different categories or to identify specific pathologies such as benign and malignant conditions of the ovary and cervix. AI can also help differentiate normal from abnormal studies, especially in cervical and endometrial conditions. In prediction, AI algorithms can find patterns in multimodal patient data and imaging results to predict the likelihood of certain outcomes, e.g., progression of a tumor or effectiveness of a particular treatment. This can help in the development of a personalized management plan and thereby improve patient outcomes. A key challenge in implementing AI is the lack of high-quality structured data. Additionally, there are ethical and legal considerations around the use of patient data, including issues of privacy and security.
Key Anatomic/Physiologic Issues and Imaging Findings/Techniques
AI can be used in the any of the pathologies affecting the uterus, ovaries, cervix, and vagina. Ultrasound, CT, and MRI scans can be efficiently utilized for AI. AI helps in 1) analysis and interpretation; 2) early detection and diagnosis of benign and malignant pathologies; 3) customized treatment planning; 4) reducing human error; 5) efficient workflow; 6) tracking disease progression; 7) research and Development; and 8) telemedicine and remote consultations.
Conclusion
AI has made significant advancements in various fields of medicine, including gynecological imaging. AI's role in gynecological imaging is currently an active area of research and development and potentially alters the current gynecological imaging.
