Development of a dual-modality, dual-view smartphone-based imaging system for oral cancer detection

Uthoff, Ross D.; Song, Bofan; Birur, Praveen; Kuriakose, Moni Abraham; Sunny, Sumsum; Suresh, Amritha; Patrick, Sanjana; Anbarani, Afarin; Spires, Oliver; Wilder‐Smith, Petra; Liang, Rongguang

doi:10.1117/12.2296435

Cited by 13 publications

(26 citation statements)

References 28 publications

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“…To address the need for oral cancer screening in high-risk populations, we have developed a low-cost, point-of-care smartphone-based system (Fig 1). The dual-view, oral cancer screening device augments a commercially available Android smartphone (LG G4, LG, Seoul, South Korea) for AFI and white light imaging (WLI) both internal to the oral cavity with an intraoral probe, and external with a whole mouth imaging module [57]. The whole cavity imaging module provides a wide field of view (FOV) image for assessment of the patient’s overall oral health.…”

Section: Methodsmentioning

confidence: 99%

Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities

et al. 2018

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Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructure, implements autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform, enabling early detection of pre-cancerous and cancerous lesions in the oral cavity with the potential to reduce morbidity, mortality, and overall healthcare costs. Using a custom Android application, this device synchronizes external light-emitting diode (LED) illumination and image capture for AFI and WLI. Data is uploaded to a cloud server for diagnosis by a remote specialist through a web app, with the ability to transmit triage instructions back to the device and patient. Finally, with the on-site specialist’s diagnosis as the gold-standard, the remote specialist and a convolutional neural network (CNN) were able to classify 170 image pairs into ‘suspicious’ and ‘not suspicious’ with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81.25% to 94.94%.

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Section: Methodsmentioning

confidence: 99%

Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities

et al. 2018

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“…Advances in the fields of computer vision and deep learning offer powerful methods to develop adjunctive technologies that can perform an automated screening of the oral cavity and provide feedback to healthcare professionals during patient examinations as well as to individuals for self-examination. The literature on image-based automated diagnosis of oral cancer has largely focused on the use of special imaging technologies, such as optical coherence tomography [ 8 , 9 ], hyperspectral imaging [ 10 ], and autofluorescence imaging [ 11 , 12 , 13 , 14 , 15 , 16 ]. On the other hand, there have been a handful of studies performed with white-light photographic images [ 17 , 18 , 19 , 20 , 21 ], most of which focus on the identification of certain types of oral lesions.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders

Tanriver

Tekkeşın

Ergen

2021

Cancers

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Oral cancer is the most common type of head and neck cancer worldwide, leading to approximately 177,757 deaths every year. When identified at early stages, oral cancers can achieve survival rates of up to 75–90%. However, the majority of the cases are diagnosed at an advanced stage mainly due to the lack of public awareness about oral cancer signs and the delays in referrals to oral cancer specialists. As early detection and treatment remain to be the most effective measures in improving oral cancer outcomes, the development of vision-based adjunctive technologies that can detect oral potentially malignant disorders (OPMDs), which carry a risk of cancer development, present significant opportunities for the oral cancer screening process. In this study, we explored the potential applications of computer vision techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for detecting OPMD. Exploiting the advancements in deep learning, a two-stage model was proposed to detect oral lesions with a detector network and classify the detected region into three categories (benign, OPMD, carcinoma) with a second-stage classifier network. Our preliminary results demonstrate the feasibility of deep learning-based approaches for the automated detection and classification of oral lesions in real time. The proposed model offers great potential as a low-cost and non-invasive tool that can support screening processes and improve detection of OPMD.

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“…It was then tested and refined using an additional 300 data sets from our database. In the first clinical study in 92 subjects with oral lesions, the initial screening algorithm performed well, with an agreement with standard-of-care diagnosis of 80.6% (Uthoff, Song, Birur, et al 2018). After additional training, the algorithm was able to classify intraoral lesions with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81% to 95%.…”

Section: Applying Ai To Imaging To Improve Opscc Outcomesmentioning

confidence: 80%

“…After additional training, the algorithm was able to classify intraoral lesions with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81% to 95%. In another study, screening accuracy approximated 85%, whereas conventional screening accuracy by community health workers ranged from 30% to 60% (Cleveland and Robison 2013; Uthoff, Song, Birur, et al 2018; Uthoff, Song, Sunny, et al 2018). Figure 5 shows the results of a recent field study in which community health workers screened 292 individuals with increased OPSCC high risk in 3 ways: 1) by conventional clinical examination and risk factor tabulation, 2) by combining conventional clinical examination and risk factor tabulation with their visual assessment of the OPSCC probe image, and 3) using the deep learning diagnostic algorithm.…”

Section: Applying Ai To Imaging To Improve Opscc Outcomesmentioning

confidence: 96%

“…In a recent multistage, multicenter study, a very low-cost point-of-care deep learning–supported smartphone-based oral cancer probe was developed specifically for screeners in high-risk populations in remote regions with limited infrastructure (Firmalino et al 2018; Song et al 2018; Uthoff, Song, Birur, et al 2018; Uthoff, Song, Sunny, et al 2018). The probe is designed to access all areas of the oral cavity, including sites that are often inaccessible to conventional approaches and that carry high risk of HPV-related lesions (Fig.…”

Section: Applying Ai To Imaging To Improve Opscc Outcomesmentioning

confidence: 99%

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Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence

2020

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Early diagnosis is the most important determinant of oral and oropharyngeal squamous cell carcinoma (OPSCC) outcomes, yet most of these cancers are detected late, when outcomes are poor. Typically, nonspecialists such as dentists screen for oral cancer risk, and then they refer high-risk patients to specialists for biopsy-based diagnosis. Because the clinical appearance of oral mucosal lesions is not an adequate indicator of their diagnosis, status, or risk level, this initial triage process is inaccurate, with poor sensitivity and specificity. The objective of this study is to provide an overview of emerging optical imaging modalities and novel artificial intelligence–based approaches, as well as to evaluate their individual and combined utility and implications for improving oral cancer detection and outcomes. The principles of image-based approaches to detecting oral cancer are placed within the context of clinical needs and parameters. A brief overview of artificial intelligence approaches and algorithms is presented, and studies that use these 2 approaches singly and together are cited and evaluated. In recent years, a range of novel imaging modalities has been investigated for their applicability to improving oral cancer outcomes, yet none of them have found widespread adoption or significantly affected clinical practice or outcomes. Artificial intelligence approaches are beginning to have considerable impact in improving diagnostic accuracy in some fields of medicine, but to date, only limited studies apply to oral cancer. These studies demonstrate that artificial intelligence approaches combined with imaging can have considerable impact on oral cancer outcomes, with applications ranging from low-cost screening with smartphone-based probes to algorithm-guided detection of oral lesion heterogeneity and margins using optical coherence tomography. Combined imaging and artificial intelligence approaches can improve oral cancer outcomes through improved detection and diagnosis.

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Development of a dual-modality, dual-view smartphone-based imaging system for oral cancer detection

Cited by 13 publications

References 28 publications

Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities

Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities

Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders

Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence

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