ArticleTranslational vision science & technology2019
Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors.
Article in Translational vision science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Intraoperative monitoring of visual evoked potentials in patients undergoing transsphenoidal surgery for pituitary adenoma: a systematic review.BMC neurology · 2021Pooled it
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- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- Intraoperative Visual Evoked Potential Monitoring in Endoscopic Endonasal Surgery for Nonpituitary Adenoma Suprasellar Tumors.Journal of clinical neurology (Seoul, Korea) · 2026Article
- Envisioning the Future of the Neurosurgical Operating Room with the Concept of the Medical Metaverse.Journal of Korean Neurosurgical Society · 2025Article
- Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery.JMA journal · 2025Review
- Refining Flash Visual Evoked Potential Analysis in Rats: A Novel Approach Using Bilateral Epidural Electrodes.Translational vision science & technology · 2024Article
- Reliability of intraoperative visual evoked potentials (iVEPs) in monitoring visual function during endoscopic transsphenoidal surgery.Acta neurochirurgica · 2023Article
- Evolution of flash visual evoked potentials to monitor visual pathway integrity during tumor resection: illustrative cases and literature review.Neurosurgical review · 2023Review
- The classification of flash visual evoked potential based on deep learning.BMC medical informatics and decision making · 2023Article
- Machine learning and clinical neurophysiology.Journal of neurology · 2022Article
- Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging.Computer methods and programs in biomedicine · 2022Article
- The future of artificial intelligence in neurosurgery: A narrative review.Surgical neurology international · 2022Review
- Machine Learning Application of Transcranial Motor-Evoked Potential to Predict Positive Functional Outcomes of Patients.Computational intelligence and neuroscience · 2022Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDetection of the huge amount of data generated in real-time visual evoked potential (VEP) requires labor-intensive work and experienced electrophysiologists. This study aims to build an automatic VEP classification system by using a deep learning algorithm.
methodsPatients with sellar region tumor and optic chiasm compression were enrolled. Flash VEP monitoring was applied during surgical decompression. Sequential VEP images were fed into three neural network algorithms to train VEP classification models.
resultsWe included 76 patients. During surgical decompression, we observed 68 eyes with increased VEP amplitude, 47 eyes with a transient decrease, and 37 eyes without change. We generated 2,843 sequences (39,802 images) in total (887 sequences with increasing VEP, 276 sequences with decreasing VEP, and 1680 sequences without change). The model combining convolutional and recurrent neural network had the highest accuracy (87.4%; 95% confidence interval, 84.2%-90.1%). The sensitivity of predicting no change VEP, increasing VEP, and decreasing VEP was 92.6%, 78.9%, and 83.7%, respectively. The specificity of predicting no change VEP, increasing VEP, and decreasing VEP was 80.5%, 93.3%, and 100.0%, respectively. The class activation map visualization technique showed that the P2-N3-P3 complex was important in determining the output.
conclusionsWe identified three VEP responses (no change, increase, and decrease) during transsphenoidal surgical decompression of sellar region tumors. We developed a deep learning model to classify the sequential changes of intraoperative VEP. TRANSLATIONAL RELEVANCE: Our model may have the potential to be applied in real-time monitoring during surgical resection of sellar region tumors.
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