Early Prediction of Oral Precancerous Lesions Using Artificial Intelligence
EARLY PREDICTION OF ORAL PRECANCEROUS LESIONS
-
Pradeep kumar Rathinavelu
,
Vyshali Pillai
,
Pradeep Kumar Yadalam
,
Sri Shivasankari Thilagar
,
Amir Raee
,
Artak Heboyan
Abstract
Objectives: Oral potentially malignant disorders may present as white, red, or mixed red-white lesions and require accurate early recognition. This study evaluated whether texture-analysis features extracted from clinical digital images could distinguish oral precancerous lesions from other oral mucosal lesions and normal mucosa using gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and wavelet analysis.
Materials and Methods: Sixty-four clinical digital images were selected according to predefined inclusion and exclusion criteria. The dataset included leukoplakia, erythroplakia, oral submucous fibrosis, candidiasis, lichen planus, leukoderma, frictional keratosis, white spongy nevus, and normal mucosa. Regions of interest were extracted from each image, and texture features were derived using GLCM, GLRLM, and wavelet analysis. A support vector machine (SVM) classifier was then used to categorize images as oral precancerous lesions or non-precancerous/normal mucosa.
Results: GLCM yielded the highest classification accuracy (88%), followed by GLRLM (81%) and wavelet analysis (79%). The corresponding sensitivity values were 77%, 64%, and 60%, and the specificity values were 93%, 90%, and 89%, respectively. The positive predictive values were 83% for GLCM, 75% for GLRLM, and 75% for wavelet analysis.
Conclusion: GLCM-based texture features provided the best diagnostic performance in this dataset. These image-analysis methods may be useful as non-invasive adjuncts to conventional clinical examination and histopathological diagnosis; however, larger datasets and external validation are required before clinical implementation.
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29. Li M, Wang Y, Sun H. Single-Frame Infrared Image Non-Uniformity Correction Based on Wavelet Domain Noise Separation. Sensors (Basel). 2023 Oct 12;23(20):8424.
30. Diamanti N, Duxbury AJ, Ariyaratnam S, Macfarlane TV. Attitudes to biopsy procedures in general dental practice. British dental journal. 2002 May;192(10):588-92.
31. Fawcett T. An introduction to ROC analysis. Pattern recognition letters. 2006 Jun 1;27(8):861-74.
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33. Zhang Z, Huang M, Jiang Z, Chang Y, Torok J, Yin FF, et al. 4D radiomics: impact of 4D-CBCT image quality on radiomic analysis. Phys Med Biol. 2021 Feb 11;66(4):045023.
34. Yu Y, She K, Liu J, Cai X, Shi K, Kwon OM. A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution. Neural Netw. 2023 Sep;166:162-173.
35. Tong C, Pang Y, Wang Y. HIWDNet: A hybrid image-wavelet domain network for fast magnetic resonance image reconstruction. Comput Biol Med. 2022 Dec;151(Pt A):105947.
36. Bhardwaj J, Nayak A. Haar wavelet transform-based optimal Bayesian method for medical image fusion. Med Biol Eng Comput. 2020 Oct;58(10):2397-2411.
37. Zhang Y, Ding W, Pan Z, Qin J. Improved Wavelet Threshold for Image De-noising. Front Neurosci. 2019 Feb 8;13:39.
38 Sivakani R, Ansari GA. Imputation using machine learning techniques. In2020 4th International conference on computer, communication and signal processing (ICCCSP) 2020 Sep 28 (pp. 1-6). IEEE.
2. Li R, Xiao L, Gong T, Liu J, Li Y, Zhou X, et al Role of oral microbiome in oral oncogenesis, tumor progression, and metastasis. Mol Oral Microbiol. 2023 Feb;38(1):9-22.
3. Vijayakumari B, Ulaganathan G, Banumathi A, Banu AF, Kayalvizhi M. Dental cyst diagnosis using texture analysis. In2012 International Conference on Machine Vision and Image Processing (MVIP) 2012 Dec 14 (pp. 117-120). IEEE. from: http://dx.doi.org/10.1109/mvip.2012.6428774
4. Bankfalvi A, Piffko J. Prognostic and predictive factors in oral cancer: the role of the invasive tumour front. Journal of Oral Pathology & Medicine: Review article. 2000 Aug;29(7):291-8.
5. Ezhov M, Gusarev M, Golitsyna M, Yates JM, Kushnerev E, Tamimi D, et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Sci Rep. 2021 Jul 22;11(1):15006.
6. Braakhuis BJ, Leemans CR, Brakenhoff RH. A genetic progression model of oral cancer: current evidence and clinical implications. J Oral Pathol Med. 2004 Jul;33(6):317-22.
7. Feller LL, Khammissa RR, Kramer BB, Lemmer JJ. Oral squamous cell carcinoma in relation to field precancerisation: pathobiology. Cancer Cell Int. 2013 Apr 3;13(1):31.
8. Whitmore SE, Lamont RJ. Oral bacteria and cancer. PLoS Pathog. 2014 Mar 27;10(3):e1003933.
9. Patil S, Yadalam PK, Hosmani J, Khan ZA, Shankar VG, Shaukat L, et al. Modulation of oral cancer and periodontitis using chemotherapeutic agents - A narrative review. Dis Mon. 2023 Jan;69(1):101348.
10. Joseph B, Yadalam PK, Anegundi RV. Management of oral lesions following COVID-19 vaccination. Oral Dis. 2022 Nov;28 Suppl 2:2634-2635.
11. Patil S, Khan SS, Hosmani J, Khan ZA, Muruganandhan J, Mushtaq S, et al. Identification of oral immune disorders- A review and a diagnostic algorithm. Dis Mon. 2023 Jan;69(1):101350.
12. Patil S, Yadalam PK, Hosmani J, Khan ZA, Ahmed ZH, Shankar VG, et al. Oral immune-mediated disorders with malignant potential/association: An overview. Dis Mon. 2023 Jan;69(1):101349..
13. Patil S, Mustaq S, Hosmani J, Khan ZA, Yadalam PK, Ahmed ZH, et al. Advancement in therapeutic strategies for immune-mediated oral diseases. Dis Mon. 2023 Jan;69(1):101352.
14. Yadalam PK, Trivedi SS, Krishnamurthi I, Anegundi RV, Mathew A, Al Shayeb M, Narayanan JK, Jaber MA, Rajkumar R. Machine learning predicts patient tangible outcomes after dental implant surgery. IEEE Access. 2022 Dec 12;10:131481-8.
15. Kumar VS, Kumar PR, Yadalam PK, Anegundi RV, Shrivastava D, Alfurhud AA, et al. Machine learning in the detection of dental cyst, tumor, and abscess lesions. BMC Oral Health. 2023 Nov 6;23(1):833.
16. Kekre HB, Gharge SM, Sarode TK. Image segmentation of MRI images using vector quantization techniques. InThinkquest~ 2010: Proceedings of the First International Conference on Contours of Computing Technology 2011 (pp. 171-176). New Delhi: Springer India.
17. Banu AF, Kayalvizhi M, Arumugam B, Gurunathan U. Texture based classification of dental cysts. In2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) 2014 Jul 10 (pp. 1248-1253). IEEE.
18. Mohanty AK, Senapati MR, Beberta S, Lenka SK. Texture-based features for classification of mammograms using decision tree. Neural Computing and Applications. 2013 Sep;23(3):1011-7.
19. Haddon JF, Boyce JF, Strens M. Autonomous segmentation and neural network texture classification of IR image sequences. InIEE Colloquium on Image Processing for Remote Sensing 1996 Feb 13 (pp. 6-1). IET.
20. Mohammadi E, Makkiabadi B, Shamsollahi MB, Reisi P, Kermani S. Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification. Int J Neural Syst. 2022 Feb;32(2):2250004.
21. Theodorou SJ, Theodorou DJ, Sartoris DJ. Imaging characteristics of neoplasms and other lesions of the jawbones: part 1. Odontogenic tumors and tumorlike lesions. Clinical imaging. 2007 Mar 1;31(2):114-9.
22. Lin JH, Cheng TY. Dynamic clustering using support vector learning with particle swarm optimization. In18th International Conference on Systems Engineering (ICSEng'05) 2005 Aug 16 (pp. 218-223). IEEE.
23. ChandraPrabha K, Bharathi C. Texture analysis using GLCM & GLRLM feature extraction methods. Int J Res Appl Sci Eng Technol (IJRASET). 2019 May;7(5):2059-64.
24. Gadodia A, Gamanagatti S, Neyaz Z. Segmental cystic renal disease: Sonographic and CT findings. Journal of Clinical Ultrasound [Internet]. 2009 Sep;37(9):525–526. Available from: http://dx.doi.org/10.1002/jcu.20636
25. Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng [Internet]. 1998 Jun;45(6):783–794. Available from: http://dx.doi.org/10.1109/10.678613
26. Reddy TK, Kumaravel N. Wavelet based texture analysis and classification of bone lesions from dental CT. International Journal of Medical Engineering and Informatics. 2010 Jan 1;2(3):319-27.
27. Ji D, Xue X, Xu C. Truncated total variation in fractional B-spline wavelet transform for micro-CT image denoising. J Xray Sci Technol. 2023;31(3):555-572.
28. Kang E, Min J, Ye JC. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys. 2017 Oct;44(10):e360-e375.
29. Li M, Wang Y, Sun H. Single-Frame Infrared Image Non-Uniformity Correction Based on Wavelet Domain Noise Separation. Sensors (Basel). 2023 Oct 12;23(20):8424.
30. Diamanti N, Duxbury AJ, Ariyaratnam S, Macfarlane TV. Attitudes to biopsy procedures in general dental practice. British dental journal. 2002 May;192(10):588-92.
31. Fawcett T. An introduction to ROC analysis. Pattern recognition letters. 2006 Jun 1;27(8):861-74.
32. Li HS, Fan P, Peng H, Song S, Long GL. Multilevel 2-D Quantum Wavelet Transforms. IEEE Trans Cybern. 2022 Aug;52(8):8467-8480.
33. Zhang Z, Huang M, Jiang Z, Chang Y, Torok J, Yin FF, et al. 4D radiomics: impact of 4D-CBCT image quality on radiomic analysis. Phys Med Biol. 2021 Feb 11;66(4):045023.
34. Yu Y, She K, Liu J, Cai X, Shi K, Kwon OM. A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution. Neural Netw. 2023 Sep;166:162-173.
35. Tong C, Pang Y, Wang Y. HIWDNet: A hybrid image-wavelet domain network for fast magnetic resonance image reconstruction. Comput Biol Med. 2022 Dec;151(Pt A):105947.
36. Bhardwaj J, Nayak A. Haar wavelet transform-based optimal Bayesian method for medical image fusion. Med Biol Eng Comput. 2020 Oct;58(10):2397-2411.
37. Zhang Y, Ding W, Pan Z, Qin J. Improved Wavelet Threshold for Image De-noising. Front Neurosci. 2019 Feb 8;13:39.
38 Sivakani R, Ansari GA. Imputation using machine learning techniques. In2020 4th International conference on computer, communication and signal processing (ICCCSP) 2020 Sep 28 (pp. 1-6). IEEE.
| Issue | Vol 23 (Continuously Published Article-Based) | |
| Section | Original Article | |
| Keywords | ||
| Wavelet Analysis Leukoplakia Erythroplasia Oral Submucous Fibrosis Candidiasis Artificial Intelligence | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Rathinavelu P, Pillai V, Yadalam P, Thilagar S, Raee A, Heboyan A. Early Prediction of Oral Precancerous Lesions Using Artificial Intelligence. Front Dent. 2026;23.
