Role of artificial intelligence in diagnostic oral pathology-A modern approach

Krishna, A; Tanveer, Azra; Bhagirath, Pancha Venkat; Gannepalli, Ashalata

doi:10.4103/jomfp.jomfp_215_19

Cited by 39 publications

(22 citation statements)

References 31 publications

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“…C. Plato depicted an important model of brain activity. The artificial intelligence system is a framework that accepts information, discovers designs, trains using data, and generates outcomes [ 10 , 11 ]. Artificial intelligence operates in two stages: the first, which involves training, and the second, which includes testing.…”

Section: Introductionmentioning

confidence: 99%

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Rahman

Alqahtani

Aldhafferi

et al. 2022

Sensors

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Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

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Section: Introductionmentioning

confidence: 99%

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Rahman

Alqahtani

Aldhafferi

et al. 2022

Sensors

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“…Moreover, AI applications in dentistry are rapidly growing [ 11 ]. They are used for caries detection and diagnosis [ 12 ], oral cancer screening [ 13 , 14 ], improvement of brushing method [ 15 ], management of dental fear [ 16 ], automatic cleaning, shaping, and filling of the root canal [ 17 ], differential diagnosis, treatment planning, and detection of anatomical structure on dental radiographic data [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review

Issa

Olszewski

Dyszkiewicz-Konwińska

2022

IJERPH

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This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (PubMed, Medline, Web of Science, Cochrane, and Scopus). Two independent reviewers screened the titles and abstracts of the collected data, stored in EndnoteX7, against the inclusion criteria. Afterward, the included articles have been critically appraised to assess the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Seven studies were included following the deduplication and screening against exclusion criteria of the 990 initially collected articles. In total, 1288 human cone-beam computed tomography (CBCT) scans were investigated for inferior alveolar canal localization using different algorithms and compared to the results obtained from manual tracing executed by experts in the field. The reported values for diagnostic accuracy of the used algorithms were extracted. A wide range of testing measures was implemented in the analyzed studies, while some of the expected indexes were still missing in the results. Future studies should consider the new artificial intelligence guidelines to ensure proper methodology, reporting, results, and validation.

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“…Further, in general cancer awareness camps, large number of cases are diagnosed as normal. Due to this, pathologists can focus on cases diagnosed by the system as malicious [9]. Currently, there is an increasing corpus of studies being conducted on enhancing medical prognosis by utilizing Artificial Intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Shankar¹,

Lydia²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert's knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification (ISMA-AIOCC) model on Histopathological images (HIs). The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs. At the initial stage, linear smoothing filter is applied to eradicate the noise from images. Besides, MobileNet model is employed to generate a useful set of feature vectors. Then, Bidirectional Gated Recurrent Unit (BGRU) model is exploited for classification process. At the end, ISMA algorithm is utilized to fine tune the parameters involved in BGRU model. Moreover, ISMA algorithm is created by integrating traditional SMA and Chaotic Oppositional Based Learning (COBL). The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.

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Role of artificial intelligence in diagnostic oral pathology-A modern approach

Cited by 39 publications

References 31 publications

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

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