ArticleLaryngoscope investigative otolaryngology2020
Prediction of vestibular schwannoma recurrence using artificial neural network.
Article in Laryngoscope investigative otolaryngology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of artificial intelligence in predicting the clinical outcomes associated with different therapeutic approaches for vestibular schwannoma: A systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification.Scientific reports · 2026Article
- Review
- Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Cochlear signal intensity changes in vestibular schwannoma: a balanced fast field-echo MRI study.Frontiers in neurology · 2025Article
- Advancements in Skull Base Surgery: Navigating Complex Challenges with Artificial Intelligence.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.JMIR cancer · 2024Review
- Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery.The Laryngoscope · 2023Article
- Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach.World journal of otorhinolaryngology - head and neck surgery · 2023Article
- Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model.The Laryngoscope · 2023Article
- Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges.Cureus · 2023Review
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study.Cancers · 2022Article
- A Nomogram to Predict Recurrence-Free Survival Following Surgery for Vestibular Schwannoma.Frontiers in oncology · 2022Article
- Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices-A Feasibility Study.Diagnostics (Basel, Switzerland) · 2021Article
- Applications of Multivariate Statistical and Data Mining Analyses to the Search for Biomarkers of Sensorineural Hearing Loss, Tinnitus, and Vestibular Dysfunction.Frontiers in neurology · 2021Article
- Collagen Family Genes Associated with Risk of Recurrence after Radiation Therapy for Vestibular Schwannoma and Pan-Cancer Analysis.Disease markers · 2021Article
- Histopathologic Evaluation of Intralabyrinthine Schwannoma.Audiology & neuro-otology · 2021Article
- Preoperative prediction of lymph node metastasis in patients with papillary thyroid carcinoma by an artificial intelligence algorithm.American journal of translational research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectivesTo compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence.
methodsSeven hundred eighty-nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off
resultsThe overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both
conclusionThe constructed ANN model was superior to logistic regression in predicting patient-answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.