Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis

Abstract

Nerve conduction F-wave studies contain critical information about subclinical motor dysfunction which may be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial Intelligence techniques can extract time-frequency features to provide new insights into ALS diagnosis and prognosis. A retrospective analysis was performed on F-wave responses from 46,802 patients. Discrete wavelet transforms were applied to time-series waveform responses after stimulating ulnar, median, fibular, and tibial nerves. Wavelet coefficient statistics, onset age, sex, and BMI were features for training a Gradient Boosting Machine model on 40,095 (5,329 diagnosed with motor neuron disease). Model performance was tested on responses from 689 ALS patients meeting Gold Coast criteria and 689 age- and sex-matched controls. An exploratory analysis examined model performance on cohorts of patients with inclusion body myositis (IBM), cervical radiculopathy, lumbar radiculopathy, or peripheral neuropathy which can mimic ALS symptoms. Factors affecting survival were estimated through cox proportional hazards regression. The model trained using wavelet-features on the full waveform had 90% recall, 87% precision, and 88% accuracy. Similar model performance was measured using features only from the M-Wave or F-Wave. Classification probabilities for ALS patients were statistically different from the diagnoses mimicking ALS symptoms (p<0.001, ANOVA, Tukey’s post-hoc), Higher model classification probabilities of ALS, older age at onset, and family history of ALS alone or with frontotemporal dementia were factors decreasing survival. Longer diagnostic delay and upper limb onset site were factors increasing survival. Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient’s likelihood of ALS and their survival risks. Although the model can make predictions at specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS compared to other mimicking diagnoses such as IBM, cervical or lumbar radiculopathy, or peripheral neuropathy. These probabilities provide clinicians with additional information they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians diagnose ALS sooner and manage treatment based on estimated survival, which may improve outcomes and patients’ quality of life.