Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors.
Clicks: 370
ID: 85533
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
72.4
/100
370 views
250 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #14 of 134 articles by views in Journal of medical systems
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 134 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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.
| Reference Key |
vicnesh2019automatedjournal
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Vicnesh, Jahmunah;Wei, Joel Koh En;Ciaccio, Edward J;Oh, Shu Lih;Bhagat, Govind;Lewis, Suzanne K;Green, Peter H;Acharya, U Rajendra; |
| Journal | Journal of medical systems |
| Year | 2019 |
| DOI |
10.1007/s10916-019-1285-6
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.