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-specific factors may lead to the development of instability so that we can target these patients up front for fusion. There are two things that can help us approach the optimal answer for individual patients. First, there are clinical registries that house important data, and the most robust contain patient-reported outcomes (PROs) and imaging data. These data are particularly useful when applying advanced computing techniques, specifically artificial intelligence (AI) and the related area of machine learning (ML) to model and predict from real data. Ultimately, these factors

T he subpial resection of brain tumors that are adjacent to critical cortical structures is a challenging operative procedure and one in which neurosurgical trainees are expected to acquire proficiency. 1 Technical errors in this complex bimanual skill include injury to adjacent normal tissues and hemorrhage from subpial vessels, which can result in significant patient morbidity. 1 Our group developed complex and realistic virtual reality (VR) tumor resection tasks to aid learners in mastering this skill. 2 , 3 We also used artificial intelligence (AI

precision. 12 – 15 Although validated software does exist to assist in the measurement of spinopelvic parameters, these continue to require user input to identify several landmarks, and therefore user experience and time are factors preventing widespread adoption. 12 , 16 – 19 There is a need for an automated tool to measure sagittal parameters accurately and independently. Artificial intelligence (AI) has shown promise in carrying out repetitive tasks performed by humans and often achieves a more accurate and reliable result. 16 , 17 , 20 – 22 This study aimed to

go further, using specialized terminologies like artificial intelligence (AI), machine learning, artificial neural network (ANN), deep learning, and natural language processing without clearly defining these concepts, 7 , 8 thus restricting the knowledge and use of this field to only a handful. Despite having access to this vast amount of data, physicians and surgeons lack the time, tools, and expertise to take full advantage of what it has to offer. 9 At the other extreme, some practitioners believe that the utilization of big data will provide

terminologies like artificial intelligence (AI), machine learning, artificial neural network (ANN), deep learning, and natural language processing without clearly defining these concepts, 7 , 8 thus restricting the knowledge and use of this field to only a handful. Despite having access to this vast amount of data, physicians and surgeons lack the time, tools, and expertise to take full advantage of what it has to offer. 9 At the other extreme, some practitioners believe that the utilization of big data will provide solutions to all healthcare-related problems and research

Artificial intelligence (AI) technology includes deep learning and machine learning algorithms. 1 , 2 Like all fields, neurosurgery has become an increasing focus of research. However, AI is not yet clinically active in neurosurgery. This is the first case in the literature of a patient whose glioma was detected by an AI algorithm during ongoing magnetic resonance imaging (MRI), followed by an operation the next day at the Gazi University Faculty of Medicine Department of Neurosurgery. The first use of AI technology in medicine was introduced into the

. Questions often arise regarding generalizability of artificial intelligence models derived from a single institution. In this study, we aimed to create a generalizable model using data from various hospitals and geographic regions that use different MR scanner vendors, magnet strengths, and imaging parameters. Although our results from a small external data set suggest model generalizability, more extensive prospective investigations are nevertheless needed to further validate model performance across global centers. Conclusions We developed an artificial intelligence

previous medical or radiological therapy and 36 healthy controls (HCs), thereby creating the largest longitudinal cohort to date for the study of brain structural changes associated with CD. With the help of high-resolution 3-T MRI and an artificial intelligence–assisted web-based autosegmentation and quantification tool, we were able to quantify the 3D structural changes in the brain with high anatomical validity. To the best of our knowledge, this study is the first longitudinal study to evaluate whole-brain changes in CD patients during short-term remission. To better

previous medical or radiological therapy and 36 healthy controls (HCs), thereby creating the largest longitudinal cohort to date for the study of brain structural changes associated with CD. With the help of high-resolution 3-T MRI and an artificial intelligence–assisted web-based autosegmentation and quantification tool, we were able to quantify the 3D structural changes in the brain with high anatomical validity. To the best of our knowledge, this study is the first longitudinal study to evaluate whole-brain changes in CD patients during short-term remission. To better