Etiology-Agnostic Diagnosis of Early Myocardial Ischemia via AI-Driven Label-Free Spectral Histopathology

Myocardial ischemia is a core pathological mechanism in diverse fatal diseases and can be triggered by multiple factors. Diagnosing early myocardial ischemia (EMI) caused by nontraditional factors (e.g., drugs or stress) remains challenging due to subtle histological changes and limited clinical awareness. Label-free infrared spectroscopic imaging of myocardial tissue enables the revelation of convergent ischemic signatures across diverse etiologies. Here, we present an artificial intelligence (AI)-based analytical strategy to investigate the molecular mechanisms underlying EMI, enabling effective diagnosis of myocardial ischemia triggered by multiple factors. The artificial neural network (ANN) model developed using infrared spectroscopic data enabled accurate diagnosis of EMI caused by traditional factors, such as obstructive coronary artery disease. The accuracy, precision, sensitivity, and the area under the curve (AUC) were 97.45%, 99.82%, 95.24%, and 0.9993, respectively. For the first time, the model's precise diagnostic capabilities were extended to nontraditional forms of ischemia, including drug-induced Kounis Syndrome (KS) and stress-induced Takotsubo Syndrome (TTS), with prediction scores greater than 84%. This etiology-agnostic strategy captures trigger-independent biomolecular signatures, overcomes the limitations of conventional histology, and enables diagnosis of a broader range of ischemic diseases. Our method highlights the potential of spectral histopathology with AI in diagnosing diverse diseases with similar pathological features, not only providing valuable insights into the application of AI in data analysis but also demonstrating distinctive advantages of infrared spectroscopic imaging in mechanistic investigations and disease diagnosis, thereby greatly advancing the field of spectral histopathological analysis.