Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population.
OBJECTIVE
Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis.


METHODS
In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor.


RESULTS
Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%.


CONCLUSIONS
The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.
Global Biomedical Technologies, Inc, 208 Otter Glen CT, Roseville, CA 95661 USA. vinitha.sree@gmail.com.
U Rajendra
UR
Acharya
S Vinitha
SV
Sree
M Muthu Rama
MM
Krishnan
Filippo
F
Molinari
Witold
W
Zieleźnik
Ricardo H
RH
Bardales
Agnieszka
A
Witkowska
Jasjit S
JS
Suri
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D005260:Female; D050031:Hashimoto Disease / Q000000981:diagnostic imaging* / Q000453:epidemiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000706:statistics & numerical data*; D011044:Poland / Q000453:epidemiology; D015995:Prevalence; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods* / Q000706:statistics & numerical data*; D055815:Young Adult
Hashimoto disease; Hashimoto thyroiditis; computer-aided-diagnosis; general ultrasound; sonography; stationary wavelet transform