Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis

Das, Dev Kumar; Bose, S. K.; Maiti, Ananda; Mitra, Bhaskar; Mukherjee, Gopeswar; Dutta, Pranab

doi:10.1016/j.tice.2018.06.004

Cited by 77 publications

(66 citation statements)

References 22 publications

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“…In nine studies, histology WSI was used to develop algorithms for evaluation of OSCC ( n = 2), 49 , 50 OPMD ( n = 4), 51 – 54 laryngeal SCC ( n = 1), 32 oropharyngeal SCC ( n = 1) 35 and multiple HNC sites ( n = 1). 55 …”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

et al. 2021

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Background This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. Methods Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009–2020). No restrictions were placed on the AI/ML method or imaging modality used. Results In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). Conclusions There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

et al. 2021

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“…In six studies, AI-based methods were used to detect oral potentially malignant disorders (OPMD) 52,54,55,57,58,62 with five of these focussing on the detection of oral submucous fibrosis (OSF) specifically. Four studies aimed to detect oral squamous cell carcinoma (OSCC) 53,56,59,60 and one study aimed to classify OPSCC 61 . Overall, seven studies were conducted in India 53,54,55,57,58,59,62 , two in China 60,61 , one in USA 52 and one in Germany 56 .…”

Section: Description Of Studiesmentioning

confidence: 99%

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review

et al. 2020

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“…This Keratinization scoring index can be used as a quantitative measure for OSCC for even very low (4x) magnification. Again Dev Kumar et al (2018) [76] proposed a method for "automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma". They used 12 layered deep convolution neural network (CNN) for the segmentation of different layers, such as keratin, epithelial, and subepithelial.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Techniques used for the Histopathological Image Analysis of Oral Cancer-A Review

Panigrahi¹,

Swarnkar²

2020

TOBIOIJ

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Oral diseases are the 6th most revealed malignancy happening in head and neck regions found mainly in south Asian countries. It is the most common cancer with fourteen deaths in an hour on a yearly basis, as per the WHO oral cancer incidence in India. Due to the cost of tests, mistakes in the recognition procedure, and the enormous remaining task at hand of the cytopathologist, oral growths cannot be diagnosed promptly. This area is open to be looked into by biomedical analysts to identify it at an early stage. At present, with the advent of entire slide computerized scanners and tissue histopathology, there is a gigantic aggregation of advanced digital histopathological images, which has prompted the necessity for their analysis. A lot of computer aided analysis techniques have been developed by utilizing machine learning strategies for prediction and prognosis of cancer. In this review paper, first various steps of obtaining histopathological images, followed by the visualization and classification done by the doctors are discussed. As machine learning techniques are well known, in the second part of this review, the works done for histopathological image analysis as well as other oral datasets using these strategies for growth prognosis and anticipation are discussed. Comparing the pitfalls of machine learning and how it has overcome by deep learning mostly for image recognition tasks are also discussed subsequently. The third part of the manuscript describes how deep learning is beneficial and widely used in different cancer domains. Due to the remarkable growth of deep learning and wide applicability, it is best suited for the prognosis of oral disease. The aim of this review is to provide insight to the researchers opting to work for oral cancer by implementing deep learning and artificial neural networks.

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Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis

Cited by 77 publications

References 22 publications

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review

Machine Learning Techniques used for the Histopathological Image Analysis of Oral Cancer-A Review

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