Muhannad Seyam

All Publications

• Case Report: Atypical Focal Axial Presentation of Facioscapulohumeral Muscular Dystrophy Type 1. Journal of clinical neuromuscular disease Zweber, C., Grewal, A. S., Seyam, M., Tandan, R., Waheed, W. 2026; 27 (4): 140-144

  Abstract

  ABSTRACT: Although facioscapulohumeral muscular dystrophy (FSHD) is known and functionally named for the typical findings of progressive weakness of the facial, scapular, and upper arm muscles, there is a growing body of evidence describing atypical presentations, particularly involving the midline musculature. We report 2 notable such cases of atypical FSHD presentation: (1) a 36-year-old postpartum woman with sparing of the usually involved muscles, but who was found to have significant atrophy of the lower abdominal muscles, pectus excavatum, and a positive Beevor sign and (2) a 65-year-old man with a history of spine surgery, with marked lumbar paraspinal atrophy, left scapular winging, and lordosis. The patients in both cases were genetically tested and found to have 4q35 deletion consistent with FSHD1. These cases highlight the importance of recognizing atypical presentations of FSHD.

  View details for DOI 10.1097/CND.0000000000000563

  View details for PubMedID 42226219

• Predicting Disease Progression and Survival in Amyotrophic Lateral Sclerosis. Muscle & nerve Seyam, M., Morelli, K. H., Waheed, W., van den Berg, L. H., Tandan, R. 2026

  Abstract

  Amyotrophic lateral sclerosis (ALS) progresses relentlessly and is characterized by a median survival of 2-5 years from symptom onset with death from respiratory failure. ALS is a complex, multi-system neurodegenerative disorder with significant phenotypic heterogeneity and markedly variable disease progression. This variability presents challenges in determining the optimal timing for therapeutic interventions, complicates clinical trial design due to lack of effective stratification methods, and makes it difficult to reliably measure the longitudinal impact of specific interventions. Accurately capturing disease progression in ALS can be challenging. We propose that early respiratory phenotyping offers a promising approach to facilitate patient stratification, improve assessments of disease progression, and predict survival.

  View details for DOI 10.1002/mus.70198

  View details for PubMedID 41866924

• Physiological and pathological roles of the thymus and value of thymectomy in myasthenia gravis: a narrative review. Mediastinum (Hong Kong, China) Waheed, W., Bacopulos, A., Seyam, M., Kooperkamp, H., Moin, M., Malik, T., Tandan, R. 2024; 8: 31

  Abstract

  Myasthenia gravis (MG) is a well-elucidated autoimmune disorder affecting the neuromuscular junction. Given the relationship between MG and thymic pathologies, with T cell and antibody-mediated pathogenesis, surgical (i.e., thymectomy) and non-surgical approaches remain a mainstay of management of the disease. This review seeks to outline the involvement of the thymus in the development of lymphocytes leading to MG.Different databases were searched exploring the role of thymectomy in treatment and outcomes in various MG patient subpopulations, including in ocular versus generalized disease, different age groups, and antibody status.Overall, the findings of multiple studies and reviews provide evidence to support the efficacy and long-term success of thymectomy in the management of MG; outcomes have included remission status, symptom severity, and need for adjunctive therapy. However, the heterogeneity in the MG population suggests that there are multiple factors that may confound the results of thymectomy and still need further examination. Separately, other autoimmune diseases develop following thymectomy, and further research is required to elucidate this susceptibility. Finally, our review will discuss the different surgical approaches for thymectomy, including their advantages, limitations, and perioperative complications.Overall, in light of the known pathogenesis and association of the thymus with MG, thymectomy remains an extremely effective approach for long-term management and improved clinical outcomes.

  View details for DOI 10.21037/med-23-43

  View details for PubMedID 38881805

  View details for PubMedCentralID PMC11177005

• Utilization of Artificial Intelligence-based Intracranial Hemorrhage Detection on Emergent Noncontrast CT Images in Clinical Workflow. Radiology. Artificial intelligence Seyam, M., Weikert, T., Sauter, A., Brehm, A., Psychogios, M. N., Blackham, K. A. 2022; 4 (2): e210168

  Abstract

  Authors implemented an artificial intelligence (AI)-based detection tool for intracranial hemorrhage (ICH) on noncontrast CT images into an emergent workflow, evaluated its diagnostic performance, and assessed clinical workflow metrics compared with pre-AI implementation. The finalized radiology report constituted the ground truth for the analysis, and CT examinations (n = 4450) before and after implementation were retrieved using various keywords for ICH. Diagnostic performance was assessed, and mean values with their respective 95% CIs were reported to compare workflow metrics (report turnaround time, communication time of a finding, consultation time of another specialty, and turnaround time in the emergency department). Although practicable diagnostic performance was observed for overall ICH detection with 93.0% diagnostic accuracy, 87.2% sensitivity, and 97.8% negative predictive value, the tool yielded lower detection rates for specific subtypes of ICH (eg, 69.2% [74 of 107] for subdural hemorrhage and 77.4% [24 of 31] for acute subarachnoid hemorrhage). Common false-positive findings included postoperative and postischemic defects (23.6%, 37 of 157), artifacts (19.7%, 31 of 157), and tumors (15.3%, 24 of 157). Although workflow metrics such as communicating a critical finding (70 minutes [95% CI: 54, 85] vs 63 minutes [95% CI: 55, 71]) were on average reduced after implementation, future efforts are necessary to streamline the workflow all along the workflow chain. It is crucial to define a clear framework and recognize limitations as AI tools are only as reliable as the environment in which they are deployed. Keywords: CT, CNS, Stroke, Diagnosis, Classification, Application Domain © RSNA, 2022.

  View details for DOI 10.1148/ryai.210168

  View details for PubMedID 35391777

  View details for PubMedCentralID PMC8980872