Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models
Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we developed Miniset-DenseSENet, a convolut...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-90881-9 |
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| author | Yuqing Xia Jenna M. Gregory Fergal M. Waldron Holly Spence Marta Vallejo |
| author_facet | Yuqing Xia Jenna M. Gregory Fergal M. Waldron Holly Spence Marta Vallejo |
| author_sort | Yuqing Xia |
| collection | DOAJ |
| description | Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we developed Miniset-DenseSENet, a convolutional neural network combining DenseNet121 with a Squeeze-and-Excitation attention mechanism, using 190 autopsy brain images from the Gregory Laboratory at the University of Aberdeen. The model distinguishes controls, ALS patients with no cognitive impairment, and ALS patients with cognitive impairment (ALS-frontotemporal dementia) with 97.37% accuracy, addressing a significant challenge in overlapping neurodegenerative disorders involving TDP-43 proteinopathy. Miniset-DenseSENet outperformed other transfer learning models, achieving a sensitivity of 1 and specificity of 0.95. These findings suggest that integrating transfer learning and attention mechanisms into neuroimaging can enhance diagnostic accuracy, enabling earlier ALS detection and improving patient stratification. This model has the potential to guide clinical decisions and support personalied therapeutic strategies. |
| format | Article |
| id | doaj-art-18d247b5d3494e1eaa87334af221e3fc |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-18d247b5d3494e1eaa87334af221e3fc2025-08-20T02:16:48ZengNature PortfolioScientific Reports2045-23222025-02-0115111310.1038/s41598-025-90881-9Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning modelsYuqing Xia0Jenna M. Gregory1Fergal M. Waldron2Holly Spence3Marta Vallejo4School of Engineering and Physical Sciences, Heriot-Watt UniversityInstitute of Medical Sciences, University of AberdeenInstitute of Medical Sciences, University of AberdeenInstitute of Medical Sciences, University of AberdeenSchool of Mathematical and Computer Sciences, Heriot-Watt UniversityAbstract Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we developed Miniset-DenseSENet, a convolutional neural network combining DenseNet121 with a Squeeze-and-Excitation attention mechanism, using 190 autopsy brain images from the Gregory Laboratory at the University of Aberdeen. The model distinguishes controls, ALS patients with no cognitive impairment, and ALS patients with cognitive impairment (ALS-frontotemporal dementia) with 97.37% accuracy, addressing a significant challenge in overlapping neurodegenerative disorders involving TDP-43 proteinopathy. Miniset-DenseSENet outperformed other transfer learning models, achieving a sensitivity of 1 and specificity of 0.95. These findings suggest that integrating transfer learning and attention mechanisms into neuroimaging can enhance diagnostic accuracy, enabling earlier ALS detection and improving patient stratification. This model has the potential to guide clinical decisions and support personalied therapeutic strategies.https://doi.org/10.1038/s41598-025-90881-9Amyotrophic lateral sclerosisTDP-43 proteinCognitive impairmentTransfer learningAttention mechanisms |
| spellingShingle | Yuqing Xia Jenna M. Gregory Fergal M. Waldron Holly Spence Marta Vallejo Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models Scientific Reports Amyotrophic lateral sclerosis TDP-43 protein Cognitive impairment Transfer learning Attention mechanisms |
| title | Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models |
| title_full | Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models |
| title_fullStr | Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models |
| title_full_unstemmed | Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models |
| title_short | Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models |
| title_sort | improving als detection and cognitive impairment stratification with attention enhanced deep learning models |
| topic | Amyotrophic lateral sclerosis TDP-43 protein Cognitive impairment Transfer learning Attention mechanisms |
| url | https://doi.org/10.1038/s41598-025-90881-9 |
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