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William B. Gormley

Researcher at Brigham and Women's Hospital

Publications -  132
Citations -  3734

William B. Gormley is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Medicine & Traumatic brain injury. The author has an hindex of 29, co-authored 120 publications receiving 2708 citations. Previous affiliations of William B. Gormley include Harvard University & Memorial Hermann Healthcare System.

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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

TL;DR: Based on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively.
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Magnetoencephalographic mapping of the language-specific cortex

TL;DR: A novel use of magnetoencephalography (MEG) for noninvasive mapping of language-specific cortex in individual patients and in healthy volunteers and finds receptive language—specific areas can be reliably activated by simple language tasks.
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Treatment of acoustic neuroma: stereotactic radiosurgery vs. microsurgery.

TL;DR: Radiosurgical treatment for acoustic neuroma is an alternative to microsurgery and is associated with a lower rate of immediate and long-term development of facial and trigeminal neuropathy, postoperative complications, and hospital stay.
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Localization of language-specific cortex by using magnetic source imaging and electrical stimulation mapping.

TL;DR: Information provided by MS imaging can be especially helpful in cases of atypical language representation, including bihemispheric representation, and location of language in areas other than those expected within the dominant hemisphere.
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Natural and Artificial Intelligence in Neurosurgery: A Systematic Review.

TL;DR: It is concluded that ML models have the potential to augment the decision‐making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting.