High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification

Vuidel, Aurore; Cousin, Loïc; Weykopf, Beatrice; Haupt, Simone; Hanifehlou, Zahra; Wiest-Daesslé, Nicolas; Segschneider, Michaela; Lee, Joohyun; Kwon, Yong-Jun; Peitz, Michael; Ogier, Arnaud; Brino, Laurent; Brüstle, Oliver; Sommer, Peter; Wilbertz, Johannes

doi:10.1016/j.stemcr.2022.09.001

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…Of the 79 studies, data and computer codes were accessible in 29% of the cases, and only two studies provided best practice reports [ 29 , 97 ]. However, 85% of the studies provided detailed descriptions of the employed AI methodologies, while 87% reported the performance metrics.…”

Section: Resultsmentioning

confidence: 99%

“…This challenge stems from several factors, including the limited availability of the original computer codes and datasets, inadequate descriptions of the methodologies used, and the partial disclosure of findings [ 122 ]. Our analysis highlights that the code utilized was available in 29% of the cases, and only 3% of studies provided in best practice reports [ 29 , 97 ]. Additionally, the application of AI in biology is characterized by its diverse empirical methods, which, although may be successful in certain conditions and in specific laboratory environments, often do not have robust theoretical validation [ 123 ].…”

Section: Discussionmentioning

confidence: 99%

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The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

Vo,

Saito,

Ida

et al. 2024

PLoS ONE

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Background Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research. Methods In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing. Results This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies. Conclusions Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

Vo,

Saito,

Ida

et al. 2024

PLoS ONE

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“…Exploitation of such 2-dimensional neuronal disease models in an imaging-based, phenotypic approach coupled to machine learning techniques allows for an accurate determination of phenotypic signatures or a neuronal fingerprint of a given cellular state. This in turn enables screening activities to identify compounds with potential to reverse a disease-related fingerprint toward a normal profile . Compounds shown to revert the phenotype in the 2-dimensional disease model and able to confirm this ability in neural organoids are candidates for drug discovery and development.…”

Section: Addressing the Challenges: The Environmental Projectmentioning

confidence: 99%

Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience

Schumann,

Andreassen,

Banaschewski

et al. 2023

JAMA Psychiatry

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ImportanceClimate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known.ObservationsA research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse.Conclusions and RelevanceThis research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.

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“…Pacal, 2022), and brain tumors (Jia and Chen, 2020). Likewise, studies have been conducted on the classification and diagnosis of the above-mentioned diseases using ML methods (Aljaddouh and Malathi, 2022;Bhattacharjee et al, 2022;Ferreira et al, 2022;Shinde et al, 2022;Swathy and Saruladha, 2022;Vankdothu and Hameed, 2022;Vuidel et al, 2022). In addition, there are also hybrid studies in the literature where ML and DL methods are used together to achieve high success in the diagnosis of diseases (Alenezi et al, 2023;Nguyen et al, 2022;Rezaee et al, 2022;Talukder et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

ÇELİK

İni̇k

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Monkeypox is a viral disease that has recently rapidly spread. Experts have trouble diagnosing the disease because it is similar to other smallpox diseases. For this reason, researchers are working on artificial intelligence-based computer vision systems for the diagnosis of monkeypox to make it easier for experts, but a professional dataset has not yet been created. Instead, studies have been carried out on datasets obtained by collecting informal images from the Internet. The accuracy of state-of-the-art deep learning models on these datasets is unknown. Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0. In experimental studies on the original and augmented datasets, MobileNet V2 achieved the highest classification accuracy of 99.25% on the augmented dataset. In contrast, the VGG-19 model achieved the highest classification accuracy with 78.82% of the original data. Considering these results, the shallow model yielded better results for the datasets with fewer images. When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

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High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification

Cited by 11 publications

References 37 publications

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

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