Analysis of Ethical Development for Public Policies in the Acquisition of AI-Based Systems

Tabares-Soto, Reinel; Bernal-Salcedo, Joshua; García-Arias, Zergio Nicolás; Ortega-Bolaños, Ricardo; Hermosilla, María; Ruz, Gonzalo A.; Arteaga-Arteaga, Harold Brayan

doi:10.4018/978-1-6684-5892-1.ch010

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…However, the ultimate goal is to identify those MCI patients who are most likely to develop AD, particularly in the early stages, where prediction is crucial for healthcare professionals in providing optimal patient care. Moreover, assembling a larger image dataset while adhering to ethical principles ( Tabares-Soto et al, 2022 ) and aiming to minimize data-induced bias in the models can positively impact the detection capabilities and, more importantly, the generalization ability of the models, which is essential for developing viable CAD systems. Furthermore, it is vital to conduct in-depth analysis of the results using visualization or interpretation techniques, such as activation maps, occlusion sensitivity, or gradient-based heat maps.…”

Section: Discussionmentioning

confidence: 99%

Classification of Alzheimer’s disease stages from magnetic resonance images using deep learning

Mora-Rubio,

Bravo-Ortíz,

Quiñones Arredondo

et al. 2023

PeerJ Computer Science

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Alzheimer’s disease (AD) is a progressive type of dementia characterized by loss of memory and other cognitive abilities, including speech. Since AD is a progressive disease, detection in the early stages is essential for the appropriate care of the patient throughout its development, going from asymptomatic to a stage known as mild cognitive impairment (MCI), and then progressing to dementia and severe dementia; is worth mentioning that everyone suffers from cognitive impairment to some degree as we age, but the relevant task here is to identify which people are most likely to develop AD. Along with cognitive tests, evaluation of the brain morphology is the primary tool for AD diagnosis, where atrophy and loss of volume of the frontotemporal lobe are common features in patients who suffer from the disease. Regarding medical imaging techniques, magnetic resonance imaging (MRI) scans are one of the methods used by specialists to assess brain morphology. Recently, with the rise of deep learning (DL) and its successful implementation in medical imaging applications, it is of growing interest in the research community to develop computer-aided diagnosis systems that can help physicians to detect this disease, especially in the early stages where macroscopic changes are not so easily identified. This article presents a DL-based approach to classifying MRI scans in the different stages of AD, using a curated set of images from Alzheimer’s Disease Neuroimaging Initiative and Open Access Series of Imaging Studies databases. Our methodology involves image pre-processing using FreeSurfer, spatial data-augmentation operations, such as rotation, flip, and random zoom during training, and state-of-the-art 3D convolutional neural networks such as EfficientNet, DenseNet, and a custom siamese network, as well as the relatively new approach of vision transformer architecture. With this approach, the best detection percentage among all four architectures was around 89% for AD vs. Control, 80% for Late MCI vs. Control, 66% for MCI vs. Control, and 67% for Early MCI vs. Control.

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Section: Discussionmentioning

confidence: 99%

Classification of Alzheimer’s disease stages from magnetic resonance images using deep learning

Mora-Rubio,

Bravo-Ortíz,

Quiñones Arredondo

et al. 2023

PeerJ Computer Science

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Biases associated with database structure for COVID-19 detection in X-ray images

Arias-Garzón

Tabares-Soto

Bernal-Salcedo

et al. 2023

Sci Rep

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Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

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Anxiety in Young People: Analysis from a Machine Learning Model

Tabares Tabares,

Vélez Álvarez,

Bernal Salcedo

et al. 2024

Preprint

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Analysis of Ethical Development for Public Policies in the Acquisition of AI-Based Systems

Cited by 3 publications

References 23 publications

Classification of Alzheimer’s disease stages from magnetic resonance images using deep learning

Classification of Alzheimer’s disease stages from magnetic resonance images using deep learning

Biases associated with database structure for COVID-19 detection in X-ray images

Anxiety in Young People: Analysis from a Machine Learning Model

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