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MedTech Outlook | Monday, June 05, 2023
Neurosurgery can pave the way for further advancements and improvements in patient care and surgical practices through AI and machine learning.
FREMONT, CA: Artificial intelligence (AI) and machine learning (ML) algorithms have witnessed significant growth in their application within neurosurgery. These algorithms differ from previous technological advancements by empowering computers to learn, reason, and solve problems—a skill set traditionally associated with human intelligence. Neurosurgery is a complex discipline that demands long hours of training, exceptional intelligence, and a unique combination of decision-making and surgical skills. Neurosurgeons often collaborate with multidisciplinary teams comprising anesthesiologists, neurologists, medical specialist nurses, and medical students.
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The required skill set encompasses various qualities such as compassion, stamina for extended work hours, precise eye-hand coordination, and manual dexterity. While neurosurgeons typically follow similar training pathways, individual skill sets can vary, leading to different patient outcomes. Furthermore, patient outcomes in neurosurgical diseases are influenced by additional factors, including age, ethnicity, economic background, and adherence to national and international guidelines. Technical errors alone contribute to a significant proportion of errors in neurosurgery. Thus, machine learning (ML) integration can help address these errors more efficiently. Preventable medical errors have been estimated to cause approximately 100,000 deaths in the United States annually, with surgical errors causing significant economic losses. However, the specific cost-effectiveness of neurosurgical procedures remains unclear.
As automation increasingly permeates various aspects of life, medicine, and surgery have not been exempt from this technological revolution. AI has been extensively incorporated into routine diagnostic and clinical investigations and laparoscopic and robotic surgeries. AI, a groundbreaking advancement, enables machines to simulate human problem-solving and decision-making capabilities by learning from vast datasets and patterns of human activities. Core subfields of AI include machine learning (ML), natural language processing (NLP), computer vision (CV), and artificial neural networks (ANNs). ML, an established AI field, enables machines to learn from experience and improve task performance without explicit programming. Studies show that the learning algorithm-based ML model can outperform a benchmark logistic regression approach in predicting patient-level outcomes of thyroidectomy, leading to more informed and patient-specific treatment decisions. NLP focuses on equipping computer systems with the ability to understand human language in written or spoken form. NLP-based models exhibited comparable prediction capabilities to manual abstraction processes and surpassed models relying solely on administrative data when identifying surgical site infections in orthopedic surgery patients. ANN, a biologically inspired subset of AI, focuses on constructing computational models that mimic the interconnected neurons in the human brain. Providers can leverage neural networks designed for facial recognition to assess the success rate of facial feminization surgeries, achieving accurate gender typing and increased confidence in femininity. Computer vision enables machines to extract and analyze visual data such as images or videos. Its utilization can facilitate the recording and comparison of surgical skills among operating surgeons, identify areas for improvement, standardize procedural skills, and accurately predict the association between postoperative complications and operation time with the complexity of surgeons' skills.
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