Temporal parameters during swallowing are analyzed for objective and quantitative evaluation of videofluoroscopic swallowing studies (VFSS). Manual analysis by clinicians is time-consuming, complicated and prone to human error during interpretation; therefore, automated analysis using deep learning has been attempted. We aimed to develop a model for the automatic measurement of various temporal parameters of swallowing using deep learning. Overall, 547 VFSS video clips were included. Seven temporal parameters were manually measured by two physiatrists as ground-truth data: oral phase duration, pharyngeal delay time, pharyngeal response time, pharyngeal transit time, laryngeal vestibule closure reaction time, laryngeal vestibule closure duration, and upper esophageal sphincter opening duration. ResNet3D was selected as the base model for the deep learning of temporal parameters. The performances of ResNet3D variants were compared with those of the VGG and I3D models used previously. The average accuracy of the proposed ResNet3D variants was from 0.901 to 0.981. The F1 scores and average precision were 0.794 to 0.941 and 0.714 to 0.899, respectively. Compared to the VGG and I3D models, our model achieved the best results in terms of accuracy, F1 score, and average precision values. Through the clinical application of this automatic model, temporal analysis of VFSS will be easier and more accurate.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579219 | PMC |
| http://dx.doi.org/10.1038/s41598-023-44802-3 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep-Learning-Assisted Understanding of the Self-Assembly of Miktoarm Star Block Copolymers.
ACS Nano
March 2025
School of Chemical Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China.
The self-assemblies of topological complex block copolymers, especially the AB type miktoarm star ones, are fascinating topics in the soft matter field, which represent typical self-assembly behaviors analogous to those of biological membranes. However, their diverse topological asymmetries and versatile spontaneous curvatures result in rather complex phase separations that deviate significantly from the common mechanisms. Thus, numerous trial-and-error experiments with tremendous parameter space and intricate relationships are needed to study their assemblies.
View Article and Find Full Text PDFRPT: An integrated root phenotyping toolbox for segmenting and quantifying root system architecture.
Plant Biotechnol J
March 2025
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
The dissection of genetic architecture for rice root system is largely dependent on phenotyping techniques, and high-throughput root phenotyping poses a great challenge. In this study, we established a cost-effective root phenotyping platform capable of analysing 1680 root samples within 2 h. To efficiently process a large number of root images, we developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits.
View Article and Find Full Text PDFAI-driven Personalized Medicine: Transforming Clinical Practice in Inflammatory Bowel Disease.
Gastroenterology
March 2025
APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
Inflammatory bowel disease (IBD) is marked by significant clinical heterogeneity, posing challenges for accurate diagnosis and personalized treatment strategies. Conventional approaches, such as endoscopy and histology, often fail to adequately and accurately predict medium and long-term outcomes, leading to suboptimal patient management. Artificial intelligence (AI) is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response.
View Article and Find Full Text PDFDeep learning based estimation of heart surface potentials.
Artif Intell Med
March 2025
Department of Advanced Computing Sciences, Maastricht University, The Netherlands. Electronic address:
Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting from body surface potentials. This is classically based on geometric information on the torso and the heart from imaging, which complicates clinical application. In this study, we aim to develop a deep learning framework to estimate heart surface potentials solely from body surface potentials, enabling wider clinical use.
View Article and Find Full Text PDFPocketDTA: A pocket-based multimodal deep learning model for drug-target affinity prediction.
Comput Biol Chem
March 2025
Scientific Research Management Department, Shanghai University, Shanghai, 200444, China. Electronic address:
Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function".
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!