Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

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In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w), using multivariate methods and artificial intelligence. We aimed to develop and validate a convolutional neural network (CNN) model for brain age prediction (PBA) using minimally processed T1w MRIs. Our model only requires one preprocessing step (i.e., image registration to MNI space), which is an advantage in comparison with previous methods that require more preprocessing steps. We used a multi-cohort dataset of cognitively healthy individuals comprising 16734 MRIs for training and evaluation. To validate our model and its interpretability, we used a multivariate model, Orthogonal Projections to Latent Structures (OPLS), which uses brain segmented cortical thicknesses and volumes. We trained and evaluated the models with the same dataset, and systematically investigated how predictions of the CNN model differ from those of the OPLS model. The validation of our model was made by testing an external dataset. The CNN and the OPLS model achieved a mean absolute error (MAE) in the testing dataset of 3.04 and 4.81 years, respectively. The model's performance in the external dataset was in the typical range of MAE found in the literature for testing sets. The CNN model revealed similar image patterns when grouped by chronological age (CA) and CNN predicted age. No significant differences were found between the oldest and youngest quartiles of age predictions by the CNN in a validation cohort of individuals with CA of 70 years old. Sensitivity maps analysis revealed that the age prediction is based mainly on the ventricles and other CSF spaces, which have been shown in the literature to reflect aging and are in accordance with the most important regions for the prediction in the OPLS model. While both the CNN and the OPLS model demonstrated acceptable performance metrics on a hold-out test set, individual predictions differed substantially, with brain age patterns of the CNN model being more comparable to the chronological age. In conclusion, our CNN model showed results comparable to the literature, using minimally processed images, which may facilitate the future implementation of brain age prediction in research and clinical settings.

Aging is a complex process that involves changes at both molecular and morphological levels. However, our understanding of how aging affects brain anatomy and function is still poor. In addition, numerous biomarkers and imaging markers, usually associated with neurodegenerative diseases such as Alzheimer's disease (AD), have been clinically used to study cognitive decline. However, the path of cognitive decline from healthy aging to a mild cognitive impairment (MCI) stage has been studied only marginally. This review presents aspects of cognitive decline assessment based on the imaging differences between individuals cognitively unimpaired and in the decline spectrum. Furthermore, we discuss the relationship between imaging markers and the change in their patterns with aging by using neuropsychological tests. Our goal is to delineate how aging has been studied by using medical imaging tools and further explore the aging brain and cognitive decline. We find no consensus among the biomarkers to assess the cognitive decline and its relationship with the cognitive decline trajectory. Brain glucose hypometabolism was found to be directly related to aging and indirectly to cognitive decline. We still need to understand how to quantify an expected hypometabolism during cognitive decline during aging. The Aβ burden should be longitudinally studied to achieve a better consensus on its association with changes in the brain and cognition decline with aging. There exists a lack of standardization of imaging markers that highlight the need for their further improvement. In conclusion, we argue that there is a lot to investigate and understand cognitive decline better and seek a window for a suitable and effective treatment strategy.

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