Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells.
Ann Neurol
; 89(6): 1226-1233, 2021 06.
Article
in En
| MEDLINE
| ID: mdl-33565152
ABSTRACT
In amyotrophic lateral sclerosis (ALS), early diagnosis is essential for both current and potential treatments. To find a supportive approach for the diagnosis, we constructed an artificial intelligence-based prediction model of ALS using induced pluripotent stem cells (iPSCs). Images of spinal motor neurons derived from healthy control subject and ALS patient iPSCs were analyzed by a convolutional neural network, and the algorithm achieved an area under the curve of 0.97 for classifying healthy control and ALS. This prediction model by deep learning algorithm with iPSC technology could support the diagnosis and may provide proactive treatment of ALS through future prospective research. ANN NEUROL 2021;891226-1233.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Early Diagnosis
/
Induced Pluripotent Stem Cells
/
Deep Learning
/
Amyotrophic Lateral Sclerosis
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Motor Neurons
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limits:
Aged
/
Female
/
Humans
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Male
/
Middle aged
Language:
En
Journal:
Ann Neurol
Year:
2021
Type:
Article
Affiliation country:
Japan