AUTHOR=Dai Congxin , Sun Bowen , Wang Renzhi , Kang Jun 

TITLE=The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.784819

DOI=10.3389/fonc.2021.784819

ISSN=2234-943X

ABSTRACT=Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. It is extraordinarily urgent to early accurately diagnosis, individualized manage, precisely predict the treatment response and prognosis of the patients with PAs. Artificial intelligence (AI) and Machine learning (ML) has garnered increasing attention to quantitatively analyze the complex medical data to improve individualized care for patients with PAs. Therefore, we critically examine the current use of AI and ML in management of patients with PAs, and propose perspectives of improvement for future uses of AI and ML in patients with PAs. AI and ML can automatically extract plenty of quantitative features based on the massive medical data; meanwhile, related diagnosis and prediction models can be developed through quantitative analysis. The previous studies suggested that AI and ML have wide application in the early accurate diagnosis, individualized treatment, predicting the response to treatments including surgery, medications and radiotherapy, and the prognosis of patients with PAs. In addition, facial imaging based AI and ML, pathological pictures based AI and ML, and surgical microscopic video based AI and ML also have been reported to assist in the management of patients with PAs. In conclusion, the current use of AI and ML models have the potential to assist doctors and patients make crucial surgical decisions by providing accurate diagnosis, response to treatment and prognosis of PAs. These AI and ML models can improve quality and safety of medical services for patients with PAs, and reduce complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for management of PA patients.