Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review

Khanagar, Sanjeev; Naik, Sachin; Kheraif, Abdulaziz A. Al; Vishwanathaiah, Satish; Maganur, Prabhadevi C; Alhazmi, Yaser Ali; Mushtaq, Shazia; Sarode, Sachin C.; Sarode, Gargi S; Zanza, Alessio; Testarelli, Luca; Patil, Shankargouda

doi:10.3390/diagnostics11061004

Cited by 56 publications

(55 citation statements)

References 36 publications

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“…As in our study, in work by Silva (2019), this feature proved useful in distinguishing the different lesions studied, with significant differences between healthy tissue and tissue with dysplasia; in the present work, a significance was obtained between OL and PVL. In this sense, it can be concluded that both entropy and the Moran Index can be used to detect changes in chromatin in premalignant lesions, a fact reinforced by the study by [20][21][22], who analyzed some nuclear characteristics and verified that the nuclear texture is an effective variable in differentiating the degrees of dysplasia in Barrett's ssophagus, in addition to being efficient in predicting progression to cancer, which, together with our results, further highlights the importance of evaluating the nuclear textures in an attempt to elucidate the pathological conditions of premalignant lesions.…”

Section: Discussionmentioning

confidence: 87%

Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques

Alshawwa

Saleh

Badr³

et al. 2022

BioMed Research International

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PVL (proliferative verrucous leukoplakia) has distinct clinical characteristics. They have a proclivity for multifocality, a high recurrence rate after treatment, and malignant transformation, and they can progress to verrucous or squamous cell carcinoma. AI can aid in the diagnosis and prognosis of cancers and other diseases. Computational algorithms can spot tissue changes that a pathologist might overlook. This method is only used in a few studies to diagnose LB and PVL. To see if their cellular nuclei differed and if this cellular compartment could classify them, researchers used a computational system and a polynomial classifier to compare OLs and PVLs. 161 OL and 3 PVL specimens in the lab were grown, photographed, and used for training and computation. Exam orders revealed patients’ sociodemographics and clinical pathologies. The nucleus was segmented using Mask R-CNN, and LB and PVL were classified using a polynomial classifier based on nucleus area, perimeter, eccentricity, orientation, solidity, entropies, and Moran Index (a measure of disorderliness). The majority of OL patients were male smokers; most PVL patients were female, with a third having malignant transformation. The neural network correctly identified cell nuclei 92.95% of the time. Except for solidity, 11 of the 13 nuclear characteristics compared between the PVL and the LB showed significant differences. The 97.6% under the curve of the polynomial classifier was used to classify the two lesions. These results demonstrate that computational methods can aid in diagnosing these two lesions.

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Section: Discussionmentioning

confidence: 87%

Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques

Alshawwa

Saleh

Badr³

et al. 2022

BioMed Research International

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“…Nowadays, artificial intelligence (AI) has gained a giant leap in dentistry, assisting clinicians in a variety of fields, e.g., detection of periapical lesions and root fractures, optimizing implant designs, diagnosis of oral cancer [ 9 , 10 , 11 ]. Deep learning is a key field of AI, which uses a learning model to extract features of the labelled dataset and eventually can predict labels on a new dataset [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status

Zhou

et al. 2021

Diagnostics

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Background: Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. This study is aimed to develop an artificial intelligence (AI) system to automatically determine the CVM status and evaluate the AI performance. Methods: A total of 1080 cephalometric radiographs, with the age of patients ranging from 6 to 22 years old, were included in the dataset (980 in training dataset and 100 in testing dataset). Two reference points and thirteen anatomical points were labelled and the cervical vertebral maturation staging (CS) was assessed by human examiners as gold standard. A convolutional neural network (CNN) model was built to train on 980 images and to test on 100 images. Statistical analysis was conducted to detect labelling differences between AI and human examiners, AI performance was also evaluated. Results: The mean labelling error between human examiners was 0.48 ± 0.12 mm. The mean labelling error between AI and human examiners was 0.36 ± 0.09 mm. In general, the agreement between AI results and the gold standard was good, with the intraclass correlation coefficient (ICC) value being up to 98%. Moreover, the accuracy of CVM staging was 71%. In terms of F1 score, CS6 stage (85%) ranked the highest accuracy. Conclusions: In this study, AI showed a good agreement with human examiners, being a useful and reliable tool in assessing the cervical vertebral maturation.

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“…With applications like low-cost screening using smartphone-based probes, the use of combined imaging and artificial intelligence approaches can improve oral cancer outcomes through improved detection and diagnosis. [27][28][29][30]…”

Section: Figure 3 Supervised Learning Methodology For Aimentioning

confidence: 99%

Artificial Intelligence and Its Application in the Early Detection of Oral Cancers

Nath¹,

Raveendran²,

Perumbure³

2022

Clin Cancer Investig J

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Dentistry has transformed, and digitization in Dentistry is now possible through Artificial Intelligence. Artificial Intelligence is an emerging field of technology, which simulate human intelligence in machines that are programmed to think like humans and mimic our actions. Although in the recent past several articles have highlighted the applications of Artificial Intelligence in screening and detecting oral cancers, less is known about how the technology helps in that aspect. In high-risk countries like India, Pakistan, Sri Lanka, Bangladesh, etc. oral cancer is the most common cancer in men, with up to 25% of all new cases. Early diagnosis of oral cancer is necessary since most patients report late and are diagnosed at a late stage of the disease, leading to a poor prognosis. Artificial intelligence can help in the early detection of oral cancer, thus helping in early diagnosis and possible help in the prediction of the prognosis of the disease. This article focuses on the lesser-known side of Artificial Intelligence, its role in the early detection of oral cancers, and how it actually can be put to use for the benefit of the dental profession and society at large. The concept of Artificial Intelligence is explained in this operative technique article with the help of the technology provider through unique diagrams for ease of understanding.

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Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review

Cited by 56 publications

References 36 publications

Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques

Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques

Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status

Artificial Intelligence and Its Application in the Early Detection of Oral Cancers

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