Ethical considerations in the early detection of Alzheimer's disease using speech and AI

Petti, Ulla; Nyrup, Rune; Skopek, Jeffrey M.; Korhonen, Anna

doi:10.1145/3593013.3594063

Cited by 4 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biased prediction models may favor or disadvantage some groups, which may be associated with misdiagnoses, improper treatment recommendations, and insufficient or unnecessary care for individuals experiencing bias. 28 , 29 , 30 In this study, we investigated the algorithmic fairness of longitudinal prediction models for AD progression. Using publicly available data from the Alzheimer Disease Neuroimaging Initiative (ADNI), 31 we had an objective of auditing the fairness of ML models for AD progression prediction.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression

Yuan,

Linn,

Hubbard

2023

JAMA Netw Open

View full text Add to dashboard Cite

ImportancePredictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.ObjectiveTo characterize the algorithmic fairness of longitudinal prediction models for AD progression.Design, Setting, and ParticipantsThis prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.ExposuresFairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.Main Outcomes and MeasuresOutcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.ResultsA total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)–MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).Conclusions and RelevanceIn this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

show abstract

Section: Introductionmentioning

confidence: 99%

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression

Yuan,

Linn,

Hubbard

2023

JAMA Netw Open

View full text Add to dashboard Cite

show abstract

Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

Hcini,

Jdey,

Dhahri

2024

Neural Process Lett

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides a comprehensive analysis of the use of deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), for the classification of AD using brain imaging data. While previous reviews have covered similar topics, this paper offers a unique perspective by providing a detailed comparison of CNN and ViT for AD classification, highlighting the strengths and limitations of each approach. Additionally, the review presents an updated and thorough analysis of the most recent studies in the field, including the latest advancements in CNN and ViT architectures, training methods, and performance evaluation metrics. Furthermore, the paper discusses the ethical considerations and challenges associated with the use of deep learning models for AD classification, such as the need for interpretability and the potential for bias. By addressing these issues, this review aims to provide valuable insights for future research and clinical applications, ultimately advancing the field of AD classification using deep learning techniques.

show abstract

NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs

Antoniak,

Naik,

Alvarado

et al. 2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

Ethical considerations in the early detection of Alzheimer's disease using speech and AI

Cited by 4 publications

References 35 publications

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression

Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs

Contact Info

Product

Resources

About