Towards better interpretable and generalizable AD detection using collective artificial intelligence

Nguyen, Huy-Dung; Clément, Michel; Mansencal, Boris; Coupé, Pierrick

doi:10.1016/j.compmedimag.2022.102171

Cited by 4 publications

(2 citation statements)

References 59 publications

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“…Further, more advanced studies may additionally use data from postural and gait analysis [25,26]. We will also draw inspiration as to the direction of further research from the work of [27][28][29].…”

Section: Directions For Further Researchmentioning

confidence: 99%

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

et al. 2023

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It is increasingly important to provide post-stroke patients with rapid access to patient-tailored assistive technologies to increase independence, mobility, and participation. Automating the selection of assistive devices based on artificial intelligence could speed up the process and improve accuracy. It would also relieve the burden on diagnosticians and therapists and speed up the introduction of new ranges by automating databases. This article compares selected machine learning classification methods in the area of post-stroke rehabilitation device selection. The article covers the specifics of the selection, the choice of classification methods, and the identification of the best one, as well as the experimental part, the description of the results, the comparison process, and directions for further research. The novelty lies both in the topic, as the choice of classification method has an impact on the accuracy of classification in the selection of medical materials, and in the manner of the comprehensive approach. The possible contribution is of great scientific and clinical relevance, but above all, it has economic and social importance, enabling post-stroke individuals to return more quickly to the community, learning, and work, and relieving the burden on the health care system.

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Section: Directions For Further Researchmentioning

confidence: 99%

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

et al. 2023

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“…The intermediate class activation map (ICAM) is a visual tool used to interpret the decision-making process of a CNN model [38]. It provides a visualization of the extracted features of the important regions of an input image that contribute to the final prediction made by the model [38][39][40]. By visualizing these regions, one can gain insights into what the model is focusing on, and whether the model uses appropriate features to make its predictions.…”

Section: F Intermediate Class Activation Mapsmentioning

confidence: 99%

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

Altabaji,

Umair,

Tan

et al. 2024

IEEE Access

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Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet's architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.

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Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury

Rahmani,

Batson,

Zimmerman

et al. 2024

BMC Neurol

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Background Mild traumatic brain injury (mTBI) can result in lasting brain damage that is often too subtle to detect by qualitative visual inspection on conventional MR imaging. Although a number of FDA-cleared MR neuroimaging tools have demonstrated changes associated with mTBI, they are still under-utilized in clinical practice. Methods We investigated a group of 65 individuals with predominantly mTBI (60 mTBI, 48 due to motor-vehicle collision, mean age 47 ± 13 years, 27 men and 38 women) with MR neuroimaging performed in a median of 37 months post-injury. We evaluated abnormalities in brain volumetry including analysis of left-right asymmetry by quantitative volumetric analysis, cerebral perfusion by pseudo-continuous arterial spin labeling (PCASL), white matter microstructure by diffusion tensor imaging (DTI), and neurometabolites via magnetic resonance spectroscopy (MRS). Results All participants demonstrated atrophy in at least one lobar structure or increased lateral ventricular volume. The globus pallidi and cerebellar grey matter were most likely to demonstrate atrophy and asymmetry. Perfusion imaging revealed significant reductions of cerebral blood flow in both occipital and right frontoparietal regions. Diffusion abnormalities were relatively less common though a subset analysis of participants with higher resolution DTI demonstrated additional abnormalities. All participants showed abnormal levels on at least one brain metabolite, most commonly in choline and N-acetylaspartate. Conclusion We demonstrate the presence of coup-contrecoup perfusion injury patterns, widespread atrophy, regional brain volume asymmetry, and metabolic aberrations as sensitive markers of chronic mTBI sequelae. Our findings expand the historic focus on quantitative imaging of mTBI with DTI by highlighting the complementary importance of volumetry, arterial spin labeling perfusion and magnetic resonance spectroscopy neurometabolite analyses in the evaluation of chronic mTBI.

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Towards better interpretable and generalizable AD detection using collective artificial intelligence

Cited by 4 publications

References 59 publications

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury

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