Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques
Article
Gudigar, Anjan, Raghavendra, U., Samanth, Jyothi, Dharmik, Chinmay, Gangavarapu, Mokshagna Rohit, Nayak, Krishnananda, Ciaccio, Edward J., Tan, Ru-San, Molinari, Filippo and Acharya, U. Rajendra. 2022. "Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques." Journal of Imaging. 8 (4). https://doi.org/10.3390/jimaging8040102
Article Title | Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques |
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ERA Journal ID | 213284 |
Article Category | Article |
Authors | Gudigar, Anjan, Raghavendra, U., Samanth, Jyothi, Dharmik, Chinmay, Gangavarapu, Mokshagna Rohit, Nayak, Krishnananda, Ciaccio, Edward J., Tan, Ru-San, Molinari, Filippo and Acharya, U. Rajendra |
Journal Title | Journal of Imaging |
Journal Citation | 8 (4) |
Article Number | 102 |
Number of Pages | 18 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2313-433X |
Digital Object Identifier (DOI) | https://doi.org/10.3390/jimaging8040102 |
Web Address (URL) | https://www.mdpi.com/2313-433X/8/4/102 |
Abstract | 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. |
Keywords | Computer-aided diagnosis tool; deep features; ResNet-50; integrated index; hypertrophic cardiomyopathy |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Manipal Academy of Higher Education, India |
Columbia University Irving Medical Center, United States | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
Polytechnic University of Turin, Italy | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Kumamoto University, Japan | |
Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1v94/novel-hypertrophic-cardiomyopathy-diagnosis-index-using-deep-features-and-local-directional-pattern-techniques
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