RESUMEN
The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.
Asunto(s)
Electroencefalografía , Epilepsia , Aprendizaje Automático , Redes Neurales de la Computación , Convulsiones , Humanos , Electroencefalografía/métodos , Convulsiones/diagnóstico , Convulsiones/fisiopatología , Epilepsia/diagnóstico , Epilepsia/fisiopatología , Adulto , Masculino , Algoritmos , Femenino , Persona de Mediana EdadRESUMEN
BACKGROUND: The modified Gartland classification is an important tool for evaluation of pediatric supracondylar humerus fractures (SCHF) because it can direct treatment decisions. Gartland type I can be managed outpatient, while emergent surgical consult occurs with type II and III. This study assesses the interobserver reliability of the Gartland classification between pediatric radiologists and orthopedic providers. METHODS: A retrospective review of 320 children diagnosed with a SCHF at a single tertiary children's hospital during 2022 was conducted. The Gartland classification documented in the radiographic report by a pediatric radiologist and the classification documented in the first encounter with an orthopedic provider was collected. Kappa value was used to assess interobserver reliability of classifications between radiologists and orthopedic providers. A second group of 76 Gartland type I SCHF from 2015, prior to our institution's implementation of structured reporting, was reviewed for comparison of unnecessary orthopedic consults at initial presentation. RESULTS: The Gartland classification has excellent interobserver reliability between radiologists and orthopedic providers with 90 % (289/320) agreement and kappa of 0.854 (confidence interval: 0.805-0.903). The most frequent disagreement that occurred was fractures classified as type II by radiology and type III by orthopedics. There were similar rates of consults for the 2015 and 2022 cohorts (p = 0.26). CONCLUSION: The Gartland classification system is a reliable and effective tool for communication between radiologists and orthopedic providers. Implementing a structured reporting system has the potential to improve triage efficiency for SCHF.
Asunto(s)
Fracturas del Húmero , Cirujanos Ortopédicos , Niño , Humanos , Reproducibilidad de los Resultados , Triaje , Fracturas del Húmero/diagnóstico por imagen , Fracturas del Húmero/cirugía , Estudios Retrospectivos , Radiólogos , Húmero/diagnóstico por imagen , Resultado del TratamientoRESUMEN
OBJECTIVES: Cervical chordoma is a rare, low-grade primary bone tumor occurring in the axial skeleton. Due to challenges in surgical exposure caused by anatomic location, patients may experience dysfunction in speech and swallowing. The objective of this study was to characterize speech and swallowing outcomes for patients undergoing surgical resection of cervical chordoma. Moreover, we detail in-depth two cases with similar initial presentations to compare prognostic factors and management strategies. METHODS: Eleven patients with histologically confirmed cervical chordoma treated between 1993 and 2020 were included in this retrospective case series. Outcomes measured included overall survival, disease-free survival, need for enteral feeds, as well as results of modified barium swallow study (MBSS) and fiberoptic laryngoscopy. RESULTS: The mean age at diagnosis was 55.9 years. The patient population was 81.8% male. Mean survival after diagnosis was 96 months. Four (36.4%) patients required post-operative MBSS and demonstrated aspiration. All four of these patients presented with tumors in the superior cervical spine and received surgeries utilizing anterior approaches. Of the four, 2 required enteral feeds long-term. Four (36.4%) patients endorsed dysphonia. One patient developed post-operative right vocal fold paresis. The remaining three patients experienced stable dysphonia pre- and post-operatively. Additionally, three (27%) patients required tracheostomy placement, two of which remained in place long-term. CONCLUSIONS: Dysphagia is a common side effect of cervical chordoma resection. It is associated with the use of an anterior approach during resection and with tumors located in the superior cervical spine. Patients with postoperative dysphagia should receive early multidisciplinary swallow rehabilitation. LEVEL OF EVIDENCE: 4 Laryngoscope, 134:3706-3712, 2024.