The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas
Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-12-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.784819/full |
| _version_ | 1818871750575783936 |
|---|---|
| author | Congxin Dai Bowen Sun Renzhi Wang Jun Kang |
| author_facet | Congxin Dai Bowen Sun Renzhi Wang Jun Kang |
| author_sort | Congxin Dai |
| collection | DOAJ |
| description | Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients. |
| first_indexed | 2024-12-19T12:27:53Z |
| format | Article |
| id | doaj.art-b827ceebbd594004a9806b781e7d85b3 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-19T12:27:53Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-b827ceebbd594004a9806b781e7d85b32022-12-21T20:21:31ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-12-011110.3389/fonc.2021.784819784819The Application of Artificial Intelligence and Machine Learning in Pituitary AdenomasCongxin Dai0Bowen Sun1Renzhi Wang2Jun Kang3Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, ChinaPituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.https://www.frontiersin.org/articles/10.3389/fonc.2021.784819/fullpituitary adenomasartificial intelligencemachine learningradiomicsindividualized treatment |
| spellingShingle | Congxin Dai Bowen Sun Renzhi Wang Jun Kang The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas Frontiers in Oncology pituitary adenomas artificial intelligence machine learning radiomics individualized treatment |
| title | The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas |
| title_full | The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas |
| title_fullStr | The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas |
| title_full_unstemmed | The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas |
| title_short | The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas |
| title_sort | application of artificial intelligence and machine learning in pituitary adenomas |
| topic | pituitary adenomas artificial intelligence machine learning radiomics individualized treatment |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.784819/full |
| work_keys_str_mv | AT congxindai theapplicationofartificialintelligenceandmachinelearninginpituitaryadenomas AT bowensun theapplicationofartificialintelligenceandmachinelearninginpituitaryadenomas AT renzhiwang theapplicationofartificialintelligenceandmachinelearninginpituitaryadenomas AT junkang theapplicationofartificialintelligenceandmachinelearninginpituitaryadenomas AT congxindai applicationofartificialintelligenceandmachinelearninginpituitaryadenomas AT bowensun applicationofartificialintelligenceandmachinelearninginpituitaryadenomas AT renzhiwang applicationofartificialintelligenceandmachinelearninginpituitaryadenomas AT junkang applicationofartificialintelligenceandmachinelearninginpituitaryadenomas |