Transformer-Based Spatio-Temporal Analysis for Classification of Aortic Stenosis Severity From Echocardiography Cine Series

Aortic stenosis (AS) is characterized by restricted motion and calcification of the aortic valve and is the deadliest valvular cardiac disease. Assessment of AS severity is typically done by expert cardiologists using Doppler measurements of valvular flow from echocardiography. However, this limits the assessment of AS to hospitals staffed with experts to provide comprehensive echocardiography service. As accurate Doppler acquisition requires significant clinical training, in this paper, we present a deep learning framework to determine the feasibility of AS detection and severity classification based only on two-dimensional echocardiographic data. We demonstrate that our proposed spatio-temporal architecture effectively and efficiently combines both anatomical features and motion of the aortic valve for AS severity classification. Our model can process cardiac echo cine series of varying length and can identify, without explicit supervision, the frames that are most informative towards the AS diagnosis. We present an empirical study on how the model learns phases of the heart cycle without any supervision and frame-level annotations. Our architecture outperforms state-of-the-art results on a private and a public dataset, achieving 95.2% and 91.5% in AS detection, and 78.1% and 83.8% in AS severity classification on the private and public datasets, respectively. Notably, due to the lack of a large public video dataset for AS, we made slight adjustments to our architecture for the public dataset. Furthermore, our method addresses common problems in training deep networks with clinical ultrasound data, such as a low signal-to-noise ratio and frequently uninformative frames. Our source code is available at: https://github.com/neda77aa/FTC.git.

Investigating differences in young- and late-onset progressive supranuclear palsy

BACKGROUND

The impact of age of onset on the presentation of progressive supranuclear palsy phenotypes is not well studied. We hypothesized that there is difference in presentation and phenotype between young- and late-onset PSP.

OBJECTIVES

Our aim was to compare phenotypes and rate of change in disability between young-onset PSP (YOPSP) and late-onset PSP (LOPSP).

METHODS

Retrospective data of patients seen in the Rossy PSP Centre from March 2014 to April 2022 with clinical diagnosis of PSP as per the MDS 2017 diagnostic criteria were examined. We used cut-off age of 65 years to categorize the patients into YOPSP and LOPSP. We compared the prevalence of phenotypes, presenting symptoms, and MDS core criteria between the two groups. The severity of disease between the two groups was measured using PSP-RS.

RESULTS

We found 107 patients with clinical diagnosis of PSP as per MDS criteria, a third were defined as YOPSP. PSP speech/language (SL) phenotype was more prevalent in YOPSP (18% vs 0%, p < 0.001). Aphasia was significantly higher in YOPSP (16% vs 1.4%, p = 0.03). The speech and language dysfunction (C1) core criteria were more prevalent in YOPSP (33.3% vs 12.2%, p = 0.05). Longitudinal analysis of PSP-RS showed worsening of bulbar total score at 6 months in YOPSP (t (38) = 2.87; p = 0.05).

CONCLUSION

Our study revealed that YOPSP are more likely to present with a speech and language variant. Our results highlight that age of onset may predict PSP phenotypes, which holds both clinical and prognostic importance.

Sex and age affect acute and persisting COVID-19 illness

Long COVID is associated with neurological and neuropsychiatric manifestations. We conducted an observational study on 97 patients with prior SARS-CoV-2 infection and persisting cognitive complaints that presented to the University Health Network Memory Clinic between October 2020 and December 2021. We assessed the main effects of sex, age, and their interaction on COVID-19 symptoms and outcomes. We also examined the relative contribution of demographics and acute COVID-19 presentation (assessed retrospectively) on persistent neurological symptoms and cognition. Among our cohort, males had higher hospitalization rates than females during the acute COVID-19 illness (18/35 (51%) vs. 15/62 (24%); P = .009). Abnormal scores on cognitive assessments post-COVID were associated with older age (AOR = 0.84; 95% CI 0.74-0.93) and brain fog during initial illness (AOR = 8.80; 95% CI 1.76-65.13). Female sex (ARR = 1.42; 95% CI 1.09-1.87) and acute shortness of breath (ARR = 1.41; 95% CI 1.09-1.84) were associated with a higher risk of experiencing more persistent short-term memory symptoms. Female sex was the only predictor associated with persistent executive dysfunction (ARR = 1.39; 95% CI 1.12-1.76) and neurological symptoms (ARR = 1.66; 95% CI 1.19-2.36). Sex differences were evident in presentations and cognitive outcomes in patients with long COVID.