Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques

• Published in: Journal of Imaging Vol. 8; no. 4; p. 102
• Main Authors: Gudigar, Anjan, Raghavendra, U., Samanth, Jyothi, Dharmik, Chinmay, Gangavarapu, Mokshagna Rohit, Nayak, Krishnananda, Ciaccio, Edward J., Tan, Ru-San, Molinari, Filippo, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06.04.2022; MDPI
• Subjects: Accuracy; Automation; Cardiomyopathy; Cardiovascular disease; Classification; Computer applications to medicine. Medical informatics; computer-aided diagnosis tool; computer-aided diagnosis tool; deep features; hypertrophic cardiomyopathy; integrated index; ResNet-50; deep features; Diabetes; Diagnosis; Electrocardiography; Electronic computers. Computer science; Heart; Human error; Hypertension; hypertrophic cardiomyopathy; Image acquisition; Image classification; integrated index; Medical imaging; Neural networks; Photography; Principal components analysis; QA75.5-76.95; R858-859.7; ResNet-50; ResNet-50; computer-aided diagnosis tool; deep features; hypertrophic cardiomyopathy; integrated index; Support vector machines; Systemic diseases; TR1-1050; Wavelet transforms; deep features; hypertrophic cardiomyopathy; integrated index; ResNet-50; computer-aided diagnosis tool
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging8040102

Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The US is operator-dependent, and its interpretation is subject to human error and variability. We proposed an automated computer-aided diagnostic tool to discriminate HCM from healthy subjects on US images. We used a local directional pattern and the ResNet-50 pretrained network to classify heart US images acquired from 62 known HCM patients and 101 healthy subjects. Deep features were ranked using Student’s t-test, and the most significant feature (SigFea) was identified. An integrated index derived from the simulation was defined as 100·log10(SigFea/2)  in each subject, and a diagnostic threshold value was empirically calculated as the mean of the minimum and maximum integrated indices among HCM and healthy subjects, respectively. An integrated index above a threshold of 0.5 separated HCM from healthy subjects with 100% accuracy in our test dataset.