Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients

Wang, Meng-Yang; Jia, Chenguang; Xu, Huanqing; Xu, Congcong; Li, Xiang; Wei, Wei; Chen, Jincao

doi:10.1007/s11596-023-2713-x

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…ChatGPT's potential in specialized medical areas was showcased through its role in assisting in establishing prognosis and supporting adjuvant therapy decisions in glioma patients, with its potential for oncology application [24][25][26][27][28]. Furthermore, the tool was explored in practical neurosurgery for research and patient care, raising ethical questions and discussing its limitations while emphasizing the importance of human verification [22,23]. Various case reports demonstrated ChatGPT's role in analyzing and reporting on specific medical cases, such as papers by Zamarud et al [3] and Montagne et al [4].…”

Section: Implications and Future Directions Of Chatgpt Utilization In...mentioning

confidence: 99%

The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework

Roman,

Al-Sharif,

AL Gharyani

2023

Cureus

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The objective of this study is to explore the use of ChatGPT (Chat-Generative Pre-Trained Transformer) in neurosurgery and its potential impact on the field. The authors aim to discuss, through a systematic review of current literature, how this rising new artificial intelligence (AI) technology may prove to be a useful tool in the future, weighing its potential benefits and limitations. The authors conducted a comprehensive and systematic literature review of the use of ChatGPT and its applications in healthcare and different neurosurgery topics. Through a systematic review of the literature, with a search strategy using the databases such as PubMed, Google Scholar, and Embase, we analyzed the advantages and limitations of using ChatGPT in neurosurgery and evaluated its potential impact. ChatGPT has demonstrated promising results in various applications, such as natural language processing, language translation, and text summarization. In neurosurgery, ChatGPT can assist in different areas such as surgical planning, image recognition, medical diagnosis, patient care, and scientific production. A total of 128 articles were retrieved from databases, where the final 22 articles were included for thorough analysis. The studies reviewed demonstrate the potential of AI and deep learning (DL), through language models such as ChatGPT, to improve the accuracy and efficiency of neurosurgical procedures, as well as diagnosis, treatment, and patient outcomes across various medical specialties, including neurosurgery. There are, however, limitations to its use, including the need for large datasets and the potential for errors in the output, which most authors concur will need human verification for the final application. Our search demonstrated the potential that ChatGPT holds for the present and future, in accordance with the studies’ authors’ findings herein analyzed and expert opinions. Further research and development are required to fully understand its capabilities and limitations. AI technology can serve as a useful tool to augment human intelligence; however, it is essential to use it in a responsible and ethical manner.

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Section: Implications and Future Directions Of Chatgpt Utilization In...mentioning

confidence: 99%

The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework

Roman,

Al-Sharif,

AL Gharyani

2023

Cureus

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“…Full texts of the remaining 10 studies were then assessed carefully. An additional five articles were excluded for the following reasons: one focused on using magnetic resonance imaging for characterizing CTS [14], another analyzed the optic nerve [15], one targeted nerve block [16], one emphasized differentiating demyelinating peripheral nerve neuropathy [17], and one aimed to predict facial nerve function in acoustic neuroma patients [18] (Table S3). A total of 5 studies were included in the final quantitative analysis.…”

Section: Literature Searchmentioning

confidence: 99%

The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis

Wu,

Lin,

Shu

et al. 2023

Diagnostics

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Background: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This meta-analysis aims to investigate the role of ultrasound radiomics in the diagnosis of CTS and compare it with other diagnostic approaches. Methods: We conducted a comprehensive search of electronic databases from inception to September 2023. The included studies were assessed for quality using the Quality Assessment Tool for Diagnostic Accuracy Studies. The primary outcome was the diagnostic performance of ultrasound radiomics compared to radiologist evaluation for diagnosing CTS. Results: Our meta-analysis included five observational studies comprising 840 participants. In the context of radiologist evaluation, the combined statistics for sensitivity, specificity, and diagnostic odds ratio were 0.78 (95% confidence interval (CI), 0.71 to 0.83), 0.72 (95% CI, 0.59 to 0.81), and 9 (95% CI, 5 to 15), respectively. In contrast, the ultrasound radiomics training mode yielded a combined sensitivity of 0.88 (95% CI, 0.85 to 0.91), a specificity of 0.88 (95% CI, 0.84 to 0.92), and a diagnostic odds ratio of 58 (95% CI, 38 to 87). Similarly, the ultrasound radiomics testing mode demonstrated an aggregated sensitivity of 0.85 (95% CI, 0.78 to 0.89), a specificity of 0.80 (95% CI, 0.73 to 0.85), and a diagnostic odds ratio of 22 (95% CI, 12 to 41). Conclusions: In contrast to assessments by radiologists, ultrasound radiomics exhibited superior diagnostic performance in detecting CTS. Furthermore, there was minimal variability in the diagnostic accuracy between the training and testing sets of ultrasound radiomics, highlighting its potential as a robust diagnostic tool in CTS.

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An Artificial Neural Network Model for Predicting Postoperative Facial Nerve Outcomes After Vestibular Schwannoma Surgery

Zohdy,

Alawieh,

Bray

et al. 2023

Neurosurgery

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BACKGROUND AND OBJECTIVES: The emergence of machine learning models has significantly improved the accuracy of surgical outcome predictions. This study aims to develop and validate an artificial neural network (ANN) model for predicting facial nerve (FN) outcomes after vestibular schwannoma (VS) surgery using the proximal-to-distal amplitude ratio (P/D) along with clinical variables. METHODS: This retrospective study included 71 patients who underwent VS resection between 2018 and 2022. At the end of surgery, the FN was stimulated at the brainstem (proximal) and internal acoustic meatus (distal) and the P/D was calculated. Postoperative FN function was assessed using the House-Brackmann grading system at discharge (short-term) and after 9–12 months (long-term). House-Brackmann grades I–II were considered good outcome, whereas grades III–VI were considered fair/poor. An ANN model was constructed, and the performance of the model was evaluated using the area under the ROC curve for internal validation and accuracy, sensitivity, specificity, and positive and negative predictive values for external validation. RESULTS: The short-term FN outcome was grades I–II in 57.7% and grades III–VI in 42.3% of patients. Initially, a model using P/D had an area under the curve of 0.906 (internal validation) and an accuracy of 89.1% (95% CI: 68.3%-98.8%) (external validation) for predicting good vs fair/poor short-term FN outcomes. The model was then refined to include only muscles with a P/D with a proximal latency between 6 and 8 ms. This improved the accuracy to 100% (95% CI: 79%-100%). Integrating clinical variables (patient's age, tumor size, and preoperative HB grade) in addition to P/D into the model did not significantly improve the predative value. A model was then created to predict the long-term FN outcome using P/D with latencies between 6 and 8 ms and had an accuracy of 90.9% (95% CI: 58.7%-99.8%). CONCLUSION: ANN models incorporating P/D can be a valuable tool for predicting FN outcomes after VS surgery. Refining the model to include P/D with latencies between 6 and 8 ms further improves the model's prediction. A user-friendly interface is provided to facilitate the implementation of this model.

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Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients

Cited by 5 publications

References 36 publications

The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework

The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework

The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis

An Artificial Neural Network Model for Predicting Postoperative Facial Nerve Outcomes After Vestibular Schwannoma Surgery

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