Early diagnosis of amyotrophic lateral sclerosis based on fasciculations in muscle ultrasonography: A machine learning approach. (August 2022)

• Record Type: Journal Article
• Authors: Fukushima, Koji; Takamatsu, Naoko; Yamamoto, Yuki; Yamazaki, Hiroki; Yoshida, Takeshi; Osaki, Yusuke; Haji, Shotaro; Fujita, Koji; Sugie, Kazuma; Izumi, Yuishin
• Is Part Of: Clinical neurophysiology. Volume 140(2022)
• Page Start: 136
• Page End: 144
• Publication Date: 2022-08
• Subjects: Amyotrophic lateral sclerosis -- Early diagnosis -- Fasciculation -- Machine learning -- Muscle ultrasonography; ALS amyotrophic lateral sclerosis -- ALSFRS-R Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised -- AUC area under the curve -- BB biceps brachii -- CI confidence interval -- FCU flexor carpi ulnaris -- FDI first dorsal interosseous -- FDP flexor digitorum profundus -- FDS flexor digitorum superficialis -- GC gastrocnemius -- MUS muscle ultrasonography -- MUS-FAST muscle ultrasonography-based fasciculation testing -- RA rectus abdominis -- SBMA spinal and bulbar muscular atrophy -- SCM sternocleidomastoid -- TA tibialis anterior -- TB triceps brachii -- VM vastus medialis; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2022.06.005
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Fasciculations were abundantly detected on muscle ultrasonography from the early stage of amyotrophic lateral sclerosis. Fasciculation in the brainstem and thoracic regions has high specificity in the diagnosis of amyotrophic lateral sclerosis. Via machine learning, we developed a muscle ultrasonography-based diagnostic tool for early-stage amyotrophic lateral sclerosis. Abstract: Objective: Although fasciculation on muscle ultrasonography (MUS) is useful in diagnosing amyotrophic lateral sclerosis (ALS), its applicability to early diagnosis remains unclear. We aimed to develop and validate diagnostic models especially beneficial to early-stage ALS via machine learning. Methods: We investigated 100 patients with ALS, including 50 with early-stage ALS within 9 months from onset, and 100 without ALS. Fifteen muscles were bilaterally observed for 10 s each and the presence of fasciculations was recorded. Hierarchical clustering and nominal logistic regression, neural network, or ensemble learning were applied to the training cohort comprising the early-stage ALS to develop MUS-based diagnostic models, and they were tested in the validation cohort comprising the later-stage ALS. Results: Fasciculations on MUS in the brainstem or thoracic region had high specificity but limited sensitivities and predictive profiles for diagnosis of ALS. A machine learning-based model comprising eight muscles in the four body regions had a high sensitivity (recall), specificity, and positive predictive value (precision) for both early- and later-stage ALS patients. Conclusions: We developed and validated MUS-fasciculation-based diagnostic models for early- and later-stage ALS. Significance: Fasciculation detected in relevant muscles on MUS can contribute to the diagnosis of ALS from the early stage.