Visually evoked potential (VEP) is widely used to detect optic neuropathy in basic research and clinical practice. Traditionally, VEP is recorded non-invasively from the surface of the skull over the visual cortex. However, its trace amplitude is highly variable, largely due to intracranial modulation and artifacts. Therefore, a safe test with a strong and stable signal is highly desirable to assess optic nerve function, particularly in neurosurgical settings and animal experiments. Minimally invasive trans-sphenoidal endoscopic recording of optic chiasmatic potential (OCP) was carried out with a titanium screw implanted onto the sphenoid bone beneath the optic chiasm in the goat, whose sphenoidal anatomy is more human-like than non-human primates. The implantation procedure was swift (within 30 min) and did not cause any detectable abnormality in fetching/moving behaviors, skull CT scans and ophthalmic tests after surgery. Compared with traditional VEP, the amplitude of OCP was 5-10 times stronger, more sensitive to weak light stimulus and its subtle changes, and was more repeatable, even under extremely low general anesthesia. Moreover, the OCP signal relied on ipsilateral light stimulation, and was abolished immediately after complete optic nerve (ON) transection. Through proof-of-concept experiments, we demonstrated several potential applications of the OCP device: (1) real-time detector of ON function, (2) detector of region-biased retinal sensitivity, and (3) therapeutic electrical stimulator for the optic nerve with low and thus safe excitation threshold. OCP developed in this study will be valuable for both vision research and clinical practice. This study also provides a safe endoscopic approach to implant skull base brain-machine interface, and a feasible testbed (goat) for evaluating safety and efficacy of skull base brain-machine interface.
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http://dx.doi.org/10.7150/thno.71164 | DOI Listing |
Hematol Oncol Clin North Am
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Division of Head and Neck/Skull Base, Department of Radiation Oncology, The Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Comprehensive Cancer Center, 460 West 10th Avenue, Columbus, OH 43210, USA. Electronic address:
This review explores the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology, focusing on computer vision (CV) and natural language processing (NLP) techniques. We examined CV-based AI/ML in digital pathology and radiomics, highlighting the prospective clinical studies demonstrating their utility. We also reviewed NLP-based AI/ML applications in clinical documentation analysis, knowledge assessment, and quality assurance.
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School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, Beijing, PR China; Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou, 450000, Henan, PR China. Electronic address:
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