Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images

Abstract

Early diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients. Optical coherence tomography (OCT) is an optical imaging modality that has been widely investigated in the field of oncology for identification of cancerous entities. Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. This literature review aimed to highlight the features of precancerous and cancerous oral lesions on OCT images and specify how AI can assist in screening and diagnosis of such pathologies.

Artificial intelligence in early diagnosis and prevention of oral cancer

“…Automated OC screening by OCT requires the progression of AI algorithms for their interpretation; hence, a continuous data feed is needed to function as ground information. 31 …”

“…Automated OC screening by OCT requires J o u r n a l P r e -p r o o f the progression of AI algorithms for their interpretation; hence, a continuous data feed is needed to function as ground information. [31] Tissue sections of head and neck cancers from different sites such as the tongue, floor of the mouth, gingivae, alveolar ridge, mandible, soft palate, supraglottis, nose, maxillary sinus, parotid gland, and thyroid were evaluated in various studies. [32][33][34] The role of ML techniques as a diagnostic tool for histology images in recognizing OSCC and a few OPMDs was highlighted in recent systematic reviews.…”

Site-Coded Oral Squamous Cell Carcinoma Evaluation by Optical Coherence Tomography (OCT): A Descriptive Pilot Study

“…It is well known that the interpretation of OCT images is strongly related to operator-ability in performing the examination and reading images. To overcome this disadvantage, “trained” machine learning systems have recently been proposed to ensure more diagnostic certainty by OCT, such as successfully validated in the ophthalmology and skin fields [ 37 ]. For oral tissue characterization, very few studies investigated computational algorithms to perform potential automated oral cancer diagnosis from OCT images, and only two investigations using in vivo methods [ 20 , 36 ] based prevalently on BM evaluation and epithelial thickness score, respectively.…”

Automatic Segmentation of Teeth, Crown–Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

“…Common applications of AI in oral diagnosis and dentomaxillofacial radiology are as follows: Oral cancer prognosis and assessment of oral cancer risk [ 45 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]; Determination of temporomandibular joint disorder progression and temporomandibular internal derangements [ 27 , 30 , 34 , 38 , 63 ]; Interpretation of conventional 2D imaging [ 31 , 64 , 65 , 66 , 67 , 68 ]; Interpretation of cone beam computed tomography and other 3D imaging methods [ 1 , 10 , 12 , 17 , 18 , 19 , 21 , 23 , 27 , 69 , 70 , 71 ]. …”

“…Oral cancer prognosis and assessment of oral cancer risk [ 45 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ];…”