イノベーションの鼓動:心臓研究にAIを活用(A Pulse of Innovation: AI at the Service of Heart Research)

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2024-04-08 コロンビア大学

心臓機能と疾患の理解、新薬の試験は複雑で時間がかかる作業でしたが、人工知能(AI)と機械学習に基づく方法論を用いることで効率的で正確な心臓機能の研究が可能になります。コロンビア工学部の研究者は、心臓細胞機能の自動解析を行う画期的な新ツール、BeatProfilerを開発しました。このソフトウェアは異なる心臓機能指標を統合し、心臓病や薬物の効果を迅速かつ客観的に評価できます。このプロジェクトは、迅速かつ正確な心臓疾患の診断の必要性に基づいており、AIソフトウェアはオープンソースとして提供され、どの研究室でも無料で利用できます。今後はさらなる応用拡大が期待されます。

<関連情報>

BeatProfiler: 心機能のマルチモーダル試験管内解析により、機械学習による疾患と薬剤の分類が可能に BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs

Youngbin Kim; Kunlun Wang; Roberta I. Lock; Trevor …
IEEE Open Journal of Engineering in Medicine and Biology  Published:05 April 2024
DOI:https://doi.org/10.1109/OJEMB.2024.3377461

Abstract

Impact Statement:
BeatProfiler rapidly quantifies contractile function and calcium handling of in vitro cardiac models enabling ML classification of cardiac disease and cardioactive drugs.

Abstract:
Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler’s accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler’s extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.

イノベーションの鼓動:心臓研究にAIを活用(A Pulse of Innovation: AI at the Service of Heart Research)

Overview of study. Left: Automated acquisition of data through a custom Python code integrated with uManager or a fluorescent imaging plate reader (FLIPR). Center: Analysis through BeatProfiler to extract traces, features, segment single beats, and generate image representations of beats. Right: Machine learning applications for disease and drug classification and deep learning model interpretation through Grad-CAM.

有機化学・薬学
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