Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors
| Autor: | U. Rajendra Acharya, Peter H.R. Green, Suzanne K. Lewis, Jahmunah Vicnesh, Govind Bhagat, Joel Koh En Wei, Shu Lih Oh, Edward J. Ciaccio |
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| Rok vydání: | 2019 |
| Předmět: |
Abdominal pain
medicine.medical_specialty Diagnostic information 020205 medical informatics Medicine (miscellaneous) Health Informatics 02 engineering and technology Disease Capsule Endoscopy Sensitivity and Specificity Video capsule endoscopy Health Information Management 0202 electrical engineering electronic engineering information engineering medicine Image Processing Computer-Assisted Humans Sampling (medicine) Intestinal Mucosa business.industry Endoscopic biopsy Predictive value Small intestinal mucosa Celiac Disease Radiology medicine.symptom business Algorithms Information Systems |
| Zdroj: | Journal of medical systems. 43(6) |
| ISSN: | 1573-689X |
| Popis: | Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease. |
| Databáze: | OpenAIRE |
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