Artificial intelligence for neurodegenerative experimental models

Marzi, Sarah J.; Schilder, Brian M.; Nott, Alexi; Frigerio, Carlo Sala; Willaime‐Morawek, Sandrine; Bucholc, Magda; Hanger, Diane P.; James, Charlotte; Lewis, Patrick A.; Lourida, Ilianna; Noble, Wendy; Rodriguez‐Algarra, Francisco; Sharif, Jalil‐Ahmad; Tsalenchuk, Maria; Winchester, Laura M.; Yaman, Ümran; Yao, Zhi; ,; Ranson, Janice M.; Llewellyn, David J.

doi:10.1002/alz.13479

Cited by 6 publications

(4 citation statements)

References 277 publications

(308 reference statements)

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“…In the contemporary landscape of biomedical research, AI has emerged as a transformative force across various domains of regenerative medicine, encompassing drug discovery, disease modeling, predictive modeling, personalized medicine, tissue engineering, cell therapy, clinical trial design, patient monitoring, patient education, and regulatory compliance [16][17][18][19][20]. Our examination specifically narrows to the integration of AI within tissue engineering.…”

Section: Introductionmentioning

confidence: 99%

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

Rafieyan,

Ansari,

Vasheghani-Farahani

2024

Biofabrication

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3D (Bio)printing is a highly effective method for fabricating tissue engineering scaffolds, renowned for their exceptional precision and control. Artificial intelligence (AI) has become a crucial technology in this field, capable of learning and replicating complex patterns that surpass human capabilities. However, the integration of AI in tissue engineering is often hampered by the lack of comprehensive and reliable data. This study addresses these challenges by providing one of the most extensive datasets on 3D-printed scaffolds. It provides the most comprehensive open-source dataset and employs various AI techniques, from unsupervised to supervised learning. This dataset includes detailed information on 1,171 scaffolds, featuring a variety of biomaterials and concentrations—including 60 biomaterials such as natural and synthesized biomaterials, crosslinkers, enzymes, etc.—along with 49 cell lines, cell densities, and different printing conditions. We used over 40 machine learning and deep learning algorithms, tuning their hyperparameters to reveal hidden patterns and predict cell response, printability, and scaffold quality. The clustering analysis using KMeans identified five distinct ones. In classification tasks, algorithms such as XGBoost, Gradient Boosting, Extra Trees Classifier, Random Forest Classifier, and LightGBM demonstrated superior performance, achieving higher accuracy and F1 scores. A fully connected neural network with six hidden layers from scratch was developed, precisely tuning its hyperparameters for accurate predictions. To promote future research, the developed dataset and the associated code are publicly available on www.github.com/saeedrafieyan/MLATE.

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Section: Introductionmentioning

confidence: 99%

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

Rafieyan,

Ansari,

Vasheghani-Farahani

2024

Biofabrication

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“…This series of eight articles provides a comprehensive overview of current applications of AI to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models, 7 drug discovery and trials optimization, 8 genetics and omics, 9 biomarkers, 10 neuroimaging, 11 prevention, 12 applied models and digital health, 13 and finally, this article on methods optimization.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

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Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care.HIGHLIGHTS Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

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“…Together, this series provides a comprehensive overview of current applications of artificial intelligence (AI) to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models 28 , drug discovery and trials optimization 29 , genetics and omics 30 , biomarkers 31 , neuroimaging (this article), prevention 32 , applied models and digital health 33 , and methods optimization 34 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Borchert

Azevedo

Badhwar

et al. 2023

Alzheimer's & Dementia

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IntroductionArtificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.MethodsWe systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.ResultsA total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.DiscussionThe literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.Highlights There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

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Artificial intelligence for neurodegenerative experimental models

Cited by 6 publications

References 277 publications

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

Artificial intelligence for dementia research methods optimization

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

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