Background: In patients with chronic benign paroxysmal positional vertigo (BPPV), i.e., chronic vestibular multicanalicular canalithiasis (CVMCC), abnormal signals are transmitted from diseased labyrinths via the healthy vestibular nuclei complex to their end organs. The vestibulo-thalamo-cortical reflex as proposed in vestibular migraine is just one of these reflexes. In a group of patients diagnosed with CVMCC otolith repositioning maneuvers specific for each semicircular canal (SCC) ameliorated pain and other symptoms in 90%. Increased awareness of CVMCC may reduce suffering and continuous medication.
Objective: To evaluate if CVMCC can be the trigger of symptoms in vestibular migraine, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and whiplash associated disorders (WAD).
Study Design: Retrospective consecutive observational cohort study.
Setting: Ambulatory at a private Otoneurology Centre.
Patients: One hundred sixty-three patients with CVMCC and a history of trauma.
Intervention: Based on the symptoms (structured symptom questionnaire), the patients are post hoc sub grouped according to the criteria of the different diagnoses.
Main Outcome Measure: Frequency of patients with CVMCC who fulfill the criteria of the different diagnoses.
Results: 98% of all patients with CVMCC fulfill the Barany Society criteria of a probable vestibular migraine; 17% fulfill the International Classification of Headache Disorders defined vestibular migraine criteria; 63% fulfill the Fukuda criteria of ME/CFS; 100% of the patients with WAD suffer from CVMCC.
Conclusion: This survey supports the hypothesis that CVMCC can be the trigger of symptoms in vestibular migraine, ME/CFS, and WAD. The actual diagnosis the patient receives is often in accordance with the patient's dominant symptom.
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Article Synopsis
- This study explores the use of artificial intelligence to enhance the diagnostic accuracy of diseases related to vertigo, focusing on two main tasks involving classification of BPPV and non-BPPV patients.
- Leveraging machine learning, specifically the XGBoost model, the researchers achieved high sensitivity and accuracy rates in distinguishing between various vertigo conditions in a cohort of nearly 3,000 patients.
- The findings suggest that implementing such AI systems can significantly improve diagnostic processes and aid clinical decision-making, especially in settings with limited resources.
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