Dataset Information

Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors.

ABSTRACT: Purpose:Detection 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. Methods:Patients 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. Results:We 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. Conclusions:We 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.

SUBMITTER: Qiao N

PROVIDER: S-EPMC6871542 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors.

Qiao Nidan N Song Mengju M Ye Zhao Z He Wenqiang W Ma Zengyi Z Wang Yongfei Y Zhang Yuyan Y Shou Xuefei X

Translational vision science & technology 20191120 6

<h4>Purpose</h4>Detection 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.<h4>Methods</h4>Patients 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 t ...[more]

PMID: 31788350

Similar Datasets

Project description:There has been increasing experience in the utilization of intraoperative magnetic resonance imaging (iMRI) for intracranial surgery. Despite this trend, only a few U.S centers have examined the use of this technology for transsphenoidal resection of tumors of the sella. We present the largest series in North America examining the role of iMRI for pituitary adenoma resection. We retrospectively reviewed our institutional experience of 59-patients who underwent transsphenoidal procedures for sellar and suprasellar tumors with iMRI guidance. Of these, 52 patients had a histological diagnosis of pituitary adenoma. The technical results of this subgroup were examined. A 1.5-T iMRI was integrated with the BrainLAB (Feldkirchen, Germany) neuronavigation system. The majority (94%) of tumors in our series were macroadenomas. Seventeen percent of tumors were confined to the sella, 49% had suprasellar extensions without involvement of the cavernous sinus, 34% had frank cavernous sinus invasion. All patients underwent at least one iMRI, and 19% required one or more additional sets of intraoperative imaging. In 58% of patients, iMRI led to the surgeon attempting more resection. A gross total resection was obtained in 67% of the patients with planned total resections. There was one case of permanent postoperative diabetes insipidus and no other instances of new hormone replacement. In summary, iMRI was most useful for tumors of the sella with and without suprasellar extension where the information from the iMRI extended the complete resection rate from 40 to 72% and 55 to 88%, respectively. As one would expect, it did not substantially increase the rate of resection of tumors with cavernous sinus invasion. Overall, iMRI was particularly useful in guiding resection safely, aiding in clinical decision making, and allowing identification and preservation of the pituitary stalk and normal pituitary gland. Limitations of the iMRI include a need for additional personnel and training as well as additional operative time, which diminishes over time as personnel learn to optimize workflow efficiency. Additional costs are mitigated in part by using the iMRI as an immediate postoperative scan. Other data emerging from our experience suggest that preservation of normal gland and thus avoidance of hypopituitarism may be improved by iMRI use, but longer follow-up periods are required to test this conclusion. iMRI can detect unsuspected complications sooner than routine postoperative imaging, potentially leading to improved outcomes. However, larger studies are needed.

Dataset Information

Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors.

Publications

Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets