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Open Access 01-12-2023 | Research

Machine learning in the detection of dental cyst, tumor, and abscess lesions

Authors: Vyshiali Sivaram Kumar, Pradeep R. Kumar, Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Deepti Shrivastava, Ahmed Ata Alfurhud, Ibrahem T. Almaktoom, Sultan Abdulkareem Ali Alftaikhah, Ahmed Hamoud L Alsharari, Kumar Chandan Srivastava

Published in: BMC Oral Health | Issue 1/2023

Abstract

Background and Objective

Dental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions.

Materials & Methods

The current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis.

Results

The findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM.

Conclusion

The GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

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Title: Machine learning in the detection of dental cyst, tumor, and abscess lesions
Authors: Vyshiali Sivaram Kumar
Pradeep R. Kumar
Pradeep Kumar Yadalam
Raghavendra Vamsi Anegundi
Deepti Shrivastava
Ahmed Ata Alfurhud
Ibrahem T. Almaktoom
Sultan Abdulkareem Ali Alftaikhah
Ahmed Hamoud L Alsharari
Kumar Chandan Srivastava
Publication date: 01-12-2023
Publisher: BioMed Central
Published in: BMC Oral Health / Issue 1/2023
Electronic ISSN: 1472-6831
DOI: https://doi.org/10.1186/s12903-023-03571-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Machine learning in the detection of dental cyst, tumor, and abscess lesions

Abstract

Background and Objective

Materials & Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background and Objective

Materials & Methods

Results

Conclusion

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