Artificial neural networks in neurosurgery.

Area of Science:

• Neurosurgery
• Artificial Intelligence
• Medical Informatics

Background:

• Artificial neural networks (ANNs) are powerful tools for analyzing complex, non-linear datasets.
• Their application in clinical decision-making, particularly in neurosurgery, warrants a comprehensive review.
• Existing literature spans diagnostic, prognostic, and treatment-related aspects of neurosurgical care.

Purpose of the Study:

• To review and synthesize published research on the application of ANNs in neurosurgery.
• To focus on the diagnostic and prognostic capabilities of ANNs in various neurosurgical conditions.
• To explore the future potential of ANNs within the field of neurosurgery.

Main Methods:

• A systematic literature review was conducted using PubMed.
• Search terms included 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification', and 'back pain'.
• Articles published in English biomedical journals between 1993 and 2013 were screened, with 50 ultimately included.

Main Results:

• ANNs demonstrated utility in diagnosing and assessing disease progression for conditions like low back pain, brain tumors, and epilepsy.
• Applications include enhancing information extraction from radiographic images, processing intracranial pressure, and real-time tumor tracking.
• ANNs showed promise in predicting outcomes for diverse neurosurgical cases, including epilepsy, spinal disorders, hydrocephalus, and trauma.

Conclusions:

• Artificial neural networks offer significant potential for enhancing diagnosis, prognosis, and outcome prediction in neurosurgery.
• Their ability to process complex data makes them valuable for clinical decision support.
• Further integration of ANNs is anticipated to advance neurosurgical practice.