Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

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.

Comparison of Efficacy and Inflammatory Response to Thermoconjunctivoplasty Performed with Cautery or Pulsed 1460 nm Laser.

Conjunctivochalasis is a degenerative condition of the conjunctiva that disrupts tear distribution and causes irritation. Thermoreduction of the redundant conjunctiva is required if symptoms are not relieved with medical therapy. Near-infrared laser treatment is a more controlled method to shrink the conjunctiva than thermocautery. This study compared tissue shrinkage, histology, and postoperative inflammation in thermoconjunctivoplasty performed on the mouse conjunctiva using either thermocautery or pulsed 1460 nm near-infrared laser irradiation. Three sets of experiments were performed on female C57BL/6J mice (n = 72, 26 per treatment group and 20 control) to assess conjunctival shrinkage, wound histology, and inflammation 3 and 10 days after treatment. Both treatments effectively shrunk the conjunctiva, but thermocautery caused greater epithelial damage. Thermocautery caused greater infiltration of neutrophils on day 3 and neutrophils and CD11b+ myeloid cells on day 10. The thermocautery group had significantly higher conjunctival expression of IL-1ß on day 3. Expression of chemokine CCL2 was higher in the conjunctiva on day 3 and tear concentrations were higher on day 7 in the laser group. These results suggest that pulsed laser treatment causes less tissue damage and postoperative inflammation than thermocautery while effectively addressing conjunctivochalasis.

Non-Invasive Tear Break-Up Detection with the Kowa DR-1α and Its Relationship to Dry Eye Clinical Severity.

The purpose of this study is to compare visual versus software detection of non-invasive tear break-up with the KOWA DR-1α tear interferometer and investigate the relationship between non-invasive tear break-up time (NIBUT) and dry eye clinical severity. Tear interferometry with the KOWA DR-1α, together with a standardized comprehensive ocular surface/tear evaluation, was performed on 348 consecutive eyes. Investigator visually detected or software detected non-invasive tear break-up and NIBUT were measured and compared on a subset of these examinations. The relationship between software-detected NIBUT and categorical dry eye severity based on irritation symptoms and corneal and conjunctival dye staining scores was determined. The sensitivity of visual (frame-by-frame) or software detected non-invasive tear break-up in eyes with tear instability (FBUT < 10) was similar (range 63−69%). NIBUT, measured visually or by software, had a correlation coefficient of 0.87. NIBUT was significantly lower in severity levels 2 and 3 compared to levels 0 + 1, and level 3 was significantly lower than level 2. In conclusion, there is a good correlation between investigator visually detected and software-detected tear break-up and tear break-up time in the KOWA DR-1α interferometric fringe images. Software-detected NIBUT is a clinically relevant measure of dry eye clinical severity.

Survey of Clinical Neurophysiology and Epilepsy Fellowship Programs in the United States During COVID-19.

PURPOSE: The COVID-19 pandemic impacted clinical practice, education, and research in Neurophysiology/Epilepsy. Although there is published literature on clinical impact, its educational impact is not well described. A national survey of Clinical Neurophysiology (CNP) and Epilepsy fellowship programs was conducted to assess the impact of COVID-19 on fellowship education. METHODS: A list of accredited Clinical Neurophysiology and Epilepsy fellowship programs was obtained from the Accreditation Council for Graduate Medical Education. Program directors at individual locations were contacted to complete a brief survey about the program and impact of COVID-19. Fellows from responding programs were subsequently invited to share their perceptions about the impact of the pandemic on their training. RESULTS: From 176 programs, 40 PDs responded (22.7%). From these 40 programs, fellows from 26 completed surveys (65.0% response). There was a reduction in EEG and epilepsy monitoring unit volumes post-COVID-19, with a trend of change for EMG, whereas continuous EEG volumes were mostly unchanged. The impact of the pandemic on training was rated as moderate to severe (≥50%) by 30.0% of PDs and 49.0% of trainees. In remarkable agreement, 20.0% of PDs and 20.4% of fellows believed that additional fellowship training was needed before graduation. Lack of fellow satisfaction was correlated with the perceived impact of the pandemic on education ( p = 0.008). CONCLUSIONS: This survey revealed a considerable impact on EEG/EMG clinical volume because of COVID-19, although continuous EEG was not as impacted. More fellows than PDs believed that training was considerably impacted by COVID-19, but a similar number thought that additional training was needed. It was unclear from this study whether the fellows' perception of educational impact was solely because of the pandemic or in addition to preexisting training deficiencies in the training programs.

A virtual COVID-19 ophthalmology rotation.

The coronavirus (COVID-19) pandemic temporarily suspended medical student involvement in clinical rotations, resulting in the need to develop virtual clinical experiences. The cancellation of clinical ophthalmology electives and away rotations reduces opportunities for exposure to the field, to network with faculty, conduct research, and prepare for residency applications. We review the literature and discuss the impact and consequences of COVID-19 on undergraduate medical education with an emphasis on ophthalmic undergraduate medical education. We also discuss innovative learning modalities used from medical schools around the world during the COVID-19 pandemic such as virtual didactics, online cases, and telehealth. Finally, we describe a novel, virtual neuro-ophthalmology elective created to educate medical students on neuro-ophthalmology foundational principles, provide research and presentation opportunities, and build relationships with faculty members. These innovative approaches represent a step forward in further improving medical education in ophthalmology during COVID-19 pandemic and beyond.