E-resources
Peer reviewed
-
Li, Zhenhua; Zhou, Langtao; bin, Xiang; Tan, Songhua; Tan, Zhiqiang; Tang, Anzhou
Japanese journal of radiology, 03/2024, Volume: 42, Issue: 3Journal Article
Objective Diagnosis of cochlear malformation on temporal bone CT images is often difficult. Our aim was to assess the utility of deep learning analysis in diagnosing cochlear malformation on temporal bone CT images. Methods A total of 654 images from 165 temporal bone CTs were divided into the training set ( n = 534) and the testing set ( n = 120). A target region that includes the area of the cochlear was extracted to create a diagnostic model. 4 models were used: ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The testing data set was subsequently analyzed using these models and by 4 doctors. Results The areas under the curve was 0.91, 0.94, 0.93, and 0.73 in ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The accuracy of ResNet10, ResNet50, and SE-ResNet50 is better than chief physician. Conclusions Deep learning technique implied a promising prospect for clinical application of artificial intelligence in the diagnosis of cochlear malformation based on CT images.
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.