Artificial intelligence driven real-time digital oral microscopy for early detection of oral cancer and potentially malignant disorders

Abstract

Confocal laser endomicroscopy (CLE) enables real-time diagnosis of oral cancer and potentially malignant disorders by in vivo microscopic tissue examination. One impediment to the widespread clinical adoption of this technology is the need for operator expertise in image interpretation. Here we review the application of AI to automatic tissue classification of CLE images and discuss the opportunities for integrating this technology to advance the adoption of real-time digital pathology thus improving speed, precision and reproducibility.

Keywords

Confocal laser endomicroscopy / oral epithelial dysplasia / oral squamous cell carcinoma / artificial intelligence / digital pathology / deep learning

References

[1] Wolk R, Lingen MW. Proceedings of the North American Society of Head and Neck Pathology Companion Meeting, New Orleans, LA, March 12, 2023: Oral Cavity Dysplasia: Why Does Histologic Grading Continue to be Contentious? Head Neck Pathol. 2023; 17:292-8.

[2] Farah CS, Dost F, Do L. Usefulness of optical fluorescence imaging in identification and triaging of oral potentially malignant disorders: A study of VELscope in the LESIONS programme. J Oral Pathol Med. 2019; 48:581-7.

[3] Lalla Y, Matias MAT, Farah CS. Assessment of oral mucosal lesions with autofluorescence imaging and reflectance spectroscopy. J Am Dent Assoc. 2016; 147:650-60.

[4] Vu A, Farah CS. Narrow band imaging: clinical applications in oral and oropharyngeal cancer. Oral Dis. 2016; 22:383-90.

[5] WHO Classification of Tumours Editorial Board [Internet]. France: IARC; c2024 [cited2024 Jan 24]. Available from:https://publications.iarc.fr/

[6] Polglase AL, McLaren WJ, Skinner SA, Kiesslich R, Neurath MF, Delaney PM. A fluorescence confocal endomicroscope for in vivo microscopy of the upper- and the lower-GI tract. Gastrointest Endosc. 2005; 62:686-95.

[7] Rangrez M, Bussau L, Ifrit K, Delaney P. Fluorescence In Vivo Endomicroscopy: High-Resolution, 3-Dimensional Confocal Laser Endomicroscopy (Part 1). Microscopy Today. 2021; 29:32-7.

[8] East JE, Vleugels JL, Roelandt P, Bhandari P, Bisschops R, Dekker E, et al. Advanced endoscopic imaging: European Society of Gastrointestinal Endoscopy (ESGE) Technology Review. Endoscopy. 2016; 48:1029-45.

[9] Trovato C, Sonzogni A, Ravizza D, Fiori G, Tamayo D, Roberto GD, et al. Confocal laser endomicroscopy for in vivo diagnosis of Barrett’s oesophagus and associated neoplasia: a pilot study conducted in a single Italian centre. Dig Liver Dis. 2013; 45:396-402.

[10] Hurlstone DP, Tiffin N, Brown SR, Baraza W, Thomson M, Cross SS. In vivo confocal laser scanning chromo-endomicroscopy of colorectal neoplasia: changing the technological paradigm. Histopathology. 2008; 52:417-26.

[11] Abramov I, Park MT, Gooldy TC, Xu Y, Lawton MT, Little AS, et al. Real-time intraoperative surgical telepathology using confocal laser endomicroscopy. Neurosurg Focus. 2022; 52:E9.

[12] Ramani RS, Tan I, Bussau L, Angel CM, McCullough M, Yap T. Confocal microscopy in oral cancer and oral potentially malignant disorders: A systematic review. Oral Dis. 2023; 29:3003-15.

[13] Sievert M, Oetter N, Mantsopoulos K, Gostian A, Mueller SK, Koch M, et al. Systematic classification of confocal laser endomicroscopy for the diagnosis of oral cavity carcinoma. Oral Oncol. 2022; 132:105978.

[14] Farah CS, Janik M, Woo S, Grew J, Slim Z, Fox SA. Dynamic real-time optical microscopy of oral mucosal lesions using confocal laser endomicroscopy. J Oral Pathol Med. 2023; 52:539-47.

[15] Ellebrecht DB, Heßler N, Schlaefer A, Gessert N. Confocal Laser Microscopy for in vivo Intraoperative Application: Diagnostic Accuracy of Investigator and Machine Learning Strategies. Visc Med. 2021; 37:533-41.

[16] Pierangelo A, Fuks D, Validire P, Benali A, Gayet B. Diagnostic accuracy of confocal laser endomicroscopy for the characterization of liver nodules. Eur J Gastroenterol Hepatol. 2017; 29:42-7.

[17] Kim I, Kang K, Song Y, Kim T. Application of Artificial Intelligence in Pathology: Trends and Challenges. Diagnostics (Basel). 2022; 12:2794.

[18] Melnyk O, Ismail A, Ghorashi NS, Heekin M, Javan R. Generative Artificial Intelligence Terminology: A Primer for Clinicians and Medical Researchers. Cureus. 2023; 15:e49890.

[19] von Chamier L, Laine RF, Henriques R. Artificial intelligence for microscopy: what you should know. Biochem Soc Trans. 2019; 47:1029-40.

[20] Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol. 2022; 35:23-32.

[21] Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020; 1213:3-21.

[22] Cai XJ. Artificial intelligence facilitates clinical management of epithelial dysplasia in multiple organs. Explor Digit Health Technol. 2024; 2:183-7.

[23] Wu Y, Han X, Su Y, Glidewell M, Daniels JS, Liu J, et al. Multiview confocal super-resolution microscopy. Nature. 2021; 600:279-84.

[24] Guleria S, Shah TU, Pulido JV, Fasullo M, Ehsan L, Lippman R, et al. Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy. Sci Rep. 2021; 11:5086.

[25] Gessert N, Bengs M, Wittig L, Drömann D, Keck T, Schlaefer A, et al. Deep transfer learning methods for colon cancer classification in confocal laser microscopy images. Int J Comput Assist Radiol Surg. 2019; 14:1837-45.

[26] Cho H, Moon D, Heo SM, Chu J, Bae H, Choi S, et al. Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy. NPJ Precis Oncol. 2024; 8:131.

[27] Ziebart A, Stadniczuk D, Roos V, Ratliff M, von Deimling A, Hänggi D, et al. Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy. Front Oncol. 2021; 11:668273.

[28] Belykh E, Miller EJ, Patel AA, Yazdanabadi MI, Martirosyan NL, Yağmurlu K, et al. Diagnostic Accuracy of a Confocal Laser Endomicroscope for In Vivo Differentiation Between Normal Injured And Tumor Tissue During Fluorescein-Guided Glioma Resection: Laboratory Investigation. World Neurosurg. 2018; 115:e337-48.

[29] Li Y, Charalampaki P, Liu Y, Yang GZ, Giannarou S. Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data. Int J Comput Assist Radiol Surg. 2018; 13:1187-99.

[30] Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J, et al. Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning. Sci Rep. 2017; 7:11979.

[31] Shavlokhova V, Sandhu S, Flechtenmacher C, Koveshazi I, Neumeier F, Padrón-Laso V, et al. Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study. J Clin Med. 2021; 10:5326.

[32] Sievert M, Aubreville M, Mueller SK, Eckstein M, Breininger K, Iro H, et al. Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision. Eur Arch Otorhinolaryngol. 2024; 281:2115-22.

[33] Yang EC, Brenes DR, Vohra IS, Schwarz RA, Williams MD, Vigneswaran N, et al. Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images. J Med Imaging (Bellingham). 2020; 7:054502.

[34] Aubreville M, Stoeve M, Oetter N, Goncalves M, Knipfer C, Neumann H, et al. Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. Int J Comput Assist Radiol Surg. 2019; 14:31-42.

[35] Yang K, Zhang H, Qiu Y, Zhai T, Zhang Z. Self-Supervised Joint Learning for pCLE Image Denoising. Sensors (Basel). 2024; 24:2853.

[36] Jabbour JM, Saldua MA, Bixler JN, Maitland KC. Confocal endomicroscopy: instrumentation and medical applications. Ann Biomed Eng. 2012; 40:378-97.

[37] Fotteler ML, Liesche-Starnecker F, Brielmaier MC, Schobel J, Gempt J, Schlegel J, et al. Socio-Organizational Impact of Confocal Laser Endomicroscopy in Neurosurgery and Neuropathology: Results from a Process Analysis and Expert Survey. Diagnostics (Basel). 2021; 11:2128.

[38] Le Pen C, Palazzo L, Napoléon B. A health economic evaluation of needle-based confocal laser endomicroscopy for the diagnosis of pancreatic cysts. Endosc Int Open. 2017; 5:E987-95.

[39] Demétrio de Souza França P, Kossatz S, Brand C, Karassawa Zanoni D, Roberts S, Guru N, et al. A phase I study of a PARP1-targeted topical fluorophore for the detection of oral cancer. Eur J Nucl Med Mol Imaging. 2021; 48:3618-30.

[40] Englhard AS, Palaras A, Volgger V, Stepp H, Mack B, Libl D, et al. Confocal laser endomicroscopy in head and neck malignancies using FITC-labelled EpCAM- and EGF-R-antibodies in cell lines and tumor biopsies. J Biophotonics. 2017; 10:1365-76.

[41] Dy A, Nguyen NJ, Meyer J, Dawe M, Shi W, Androutsos D, et al. AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer. Sci Rep. 2024; 14:1283.

[42] Guo H, Diao L, Zhou X, Chen J, Zhou Y, Fang Q, et al. Artificial intelligence-based analysis for immunohistochemistry staining of immune checkpoints to predict resected non-small cell lung cancer survival and relapse. Transl Lung Cancer Res. 2021; 10:2452-74.

[43] Jiang Y, Chen J, Gong C, Wang TD, Seibel EJ. Deep-Learning-Based Real-Time and Automatic Target-to-Background Ratio Calculation in Fluorescence Endoscopy for Cancer Detection and Localization. Diagnostics (Basel). 2022; 12:2031.