Seyed Farzad Maroufi, Yücel Doğruel, Ahmad Pour-Rashidi, Gurkirat S Kohli, Colson Tomberlin Parker, Tatsuya Uchida, Mohamed Z Asfour, Clara Martin, Mariagrazia Nizzola, Alessandro De Bonis, Mamdouh Tawfik-Helika, Amin Tavallai, Aaron A Cohen-Gadol, Paolo Palmisciano
{"title":"Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.","authors":"Seyed Farzad Maroufi, Yücel Doğruel, Ahmad Pour-Rashidi, Gurkirat S Kohli, Colson Tomberlin Parker, Tatsuya Uchida, Mohamed Z Asfour, Clara Martin, Mariagrazia Nizzola, Alessandro De Bonis, Mamdouh Tawfik-Helika, Amin Tavallai, Aaron A Cohen-Gadol, Paolo Palmisciano","doi":"10.1007/s11102-023-01369-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations.</p><p><strong>Methods: </strong>PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies.</p><p><strong>Results: </strong>Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately.</p><p><strong>Conclusion: </strong>AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.</p>","PeriodicalId":20202,"journal":{"name":"Pituitary","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pituitary","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11102-023-01369-6","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/6 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations.
Methods: PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies.
Results: Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately.
Conclusion: AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.
期刊介绍:
Pituitary is an international publication devoted to basic and clinical aspects of the pituitary gland. It is designed to publish original, high quality research in both basic and pituitary function as well as clinical pituitary disease.
The journal considers:
Biology of Pituitary Tumors
Mechanisms of Pituitary Hormone Secretion
Regulation of Pituitary Function
Prospective Clinical Studies of Pituitary Disease
Critical Basic and Clinical Reviews
Pituitary is directed at basic investigators, physiologists, clinical adult and pediatric endocrinologists, neurosurgeons and reproductive endocrinologists interested in the broad field of the pituitary and its disorders. The Editorial Board has been drawn from international experts in basic and clinical endocrinology. The journal offers a rapid turnaround time for review of manuscripts, and the high standard of the journal is maintained by a selective peer-review process which aims to publish only the highest quality manuscripts. Pituitary will foster the publication of creative scholarship as it pertains to the pituitary and will provide a forum for basic scientists and clinicians to publish their high quality pituitary-related work.