TY - JOUR
T1 - Current status of artificial intelligence technologies in pituitary adenoma surgery
T2 - a scoping review
AU - Maroufi, Seyed Farzad
AU - Dogruel, Yuecel
AU - Pour-Rashidi, Ahmad
AU - Kohli, Gurkirat S.
AU - Parker, Colson Tomberlin
AU - Uchida, Tatsuya
AU - Asfour, Mohamed Z.
AU - Martin, Clara
AU - Nizzola, Mariagrazia
AU - De Bonis, Alessandro
AU - Tawfik-Helika, Mamdouh
AU - Tavallai, Amin
AU - Cohen-Gadol, Aaron A.
AU - Palmisciano, Paolo
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Neuro-oncology
KW - Pituitary surgery
KW - Skull base
UR - http://www.scopus.com/inward/record.url?scp=85181465242&partnerID=8YFLogxK
U2 - 10.1007/s11102-023-01369-6
DO - 10.1007/s11102-023-01369-6
M3 - Review article
C2 - 38183582
SN - 1386-341X
VL - 27
SP - 91
EP - 128
JO - Pituitary
JF - Pituitary
IS - 2
ER -