scholarly journals Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence

2020 ◽  
Vol 99 (3) ◽  
pp. 241-248 ◽  
Author(s):  
B. Ilhan ◽  
K. Lin ◽  
P. Guneri ◽  
P. Wilder-Smith
Keyword(s):  
Artificial Intelligence ◽  
Oral Cancer ◽  
Low Cost ◽  
Oral Lesion ◽  
Imaging Modalities ◽  
Risk Level ◽  
Cancer Outcomes ◽  
Clinical Appearance ◽  
Considerable Impact ◽  
Detection And Diagnosis

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.

scholarly journals Artificial Intelligence in Diagnosis of Oral Potentially Malignant Lesions- Need of the Hour

2021 ◽  
pp. 83-90
Author(s):  
Abhilasha Chapade ◽  
Kumar Gaurav Chhabra ◽  
Amit Reche ◽  
Priyanka Paul Madhu
Keyword(s):  
Artificial Intelligence ◽  
Oral Cancer ◽  
Low Cost ◽  
Cancer Outcomes ◽  
Technological Breakthrough ◽  
Potentially Malignant ◽  
Oral Cancer Screening ◽  
Diagnosis Accuracy ◽  
Malignant Lesions ◽  
Detection And Diagnosis

Artificial intelligence (AI) is a technological breakthrough that is rapidly progressing and has captivated the minds of researchers all over the world. AI can be used to make a diagnosis of oral cavity lesions, detect and identify suspicious changed oral mucosa undergoing premalignant and malignant transformations. The purpose of this review is to give a comprehensive summary of developing optical imaging technologies, innovative artificial intelligence-based techniques. The concepts of image-based techniques for identifying oral cancer are defined in terms of clinical requirements and features. Although artificial intelligence (AI) is beginning to have a significant impact on increasing diagnosis accuracy in a variety of fields of medicine, there has been limited research on oral cancer to date. These results suggest that combining artificial intelligence with imaging can improve oral cancer outcomes, applications ranging from very low-cost oral cancer screening with Smartphone-based probes to algorithm-guided identification premalignant lesion heterogeneity and margins using optical coherence tomography. Oral cancer outcomes can be improved by combining imaging and artificial intelligence technologies for better detection and diagnosis.

Tenants' Satisfaction in High Residential Building

2020 ◽  
Vol 17 (1) ◽  
pp. 41
Author(s):  
UMMU SHOLEHAH MOHD NOR
Keyword(s):  
Urban Areas ◽  
Residential Buildings ◽  
Low Cost ◽  
Residential Building ◽  
Real Estate Market ◽  
The Real Estate ◽  
Level Of Satisfaction ◽  
Considerable Impact ◽  
Residential Living ◽  
Medium Cost

High residential living in Malaysia has not been widely given a significant emphasises in literature despite its increasing scale and significance in the real estate market. The significance of high rise is mainly due the increasing rate of migration from rural to urban. It is estimated a total of 77.2 percent of the Malaysian population lived in urban areas in 2020. Approximately, 30 percent of this urban population lives in strata housing. These percentages are predicted to continue to increase in the future. The emergence of high residential building has been argued as confronting various problems which has considerable impact on this life style. Satisfaction is an important outcome of living in one’s dwelling, although it is not the only consideration. High residential building in Malaysia encountered numerous problems in term of management aspects, legislation aspects, and residents’ satisfaction. The purpose of this paper is to investigate the tenants’ satisfaction living in high residential buildings in Klang Valley. The questionnaires survey is conducted amongst 276 tenants at low cost and medium cost HRB using random sampling in HRB located at areas under jurisdiction Dewan Bandaraya Kuala Lumpur (DBKL), Majlis Bandaraya Subang Jaya (MBSJ), Majlis Bandaraya Shah Alam (MBSA), Majlis Bandaraya Subang Jaya (MBSJ), Majlis Perbandaran Selayang (MPS) and Majlis Perbandaran Ampang Jaya (MPAJ). The result from this study shows that tenant in medium cost residential building are more satisfied in term of facilities and management as compared to tenants in low cost residential building. Tenants also not disclosed to the existing act and procedure related to high residential building. In conclusion, this study suggested the Local Authority to emphasise the role of tenant. These recommendation hopefully will increase the level of satisfaction amongst the residents in HRB.

Imaging modalities to inform the detection and diagnosis of early caries

2021 ◽  
Author(s):  
Tanya Walsh ◽  
Richard Macey ◽  
Philip Riley ◽  
Anne-Marie Glenny ◽  
Falk Schwendicke ◽  
...  
Keyword(s):  
Imaging Modalities ◽  
Detection And Diagnosis ◽  
Early Caries

ARTIFICIAL INTELLIGENCE AND ITS STARRING ROLE IN DENTISTRY: A PERSPECTIVE QUESTIONNAIRE STUDY WITH REVIEW OF LITERATURE.

2021 ◽  
pp. 44-47
Author(s):  
V. Shakuntala Soujanya. V ◽  
N.Abhishek Reddy ◽  
K. Kranthi ◽  
Vinuthna Vinuthna ◽  
Prabhakar Rao
Keyword(s):  
Artificial Intelligence ◽  
Oral Cancer ◽  
Multidisciplinary Approach ◽  
Geriatric Patients ◽  
Special Care ◽  
Questionnaire Study ◽  
Review Of Literature ◽  
Advanced Technologies ◽  
Severe Infections ◽  
Dental Diseases

As there is increased preponderance and prevalence of varied diseases affecting huge population including dental diseases like severe infections secondary to pulpal and periodontal pathologies, Maxillary pathologies, Oral cancer, Osteoporosis, esthetical issues like Malocclusion, etc. which in turn should be given special care when it comes to geriatric patients and people suffering with various comorbidities where sometimes it demands for advanced technologies especially in terms of multidisciplinary approach, Articial intelligence has become a boon to dentistry making their work more simpler and accurate. This article is one of its own kind of rare questionnaire study which focus on knowing knowledge, awareness and perception of dentists of northern telangana population regarding Articial intelligence.

scholarly journals Nanotechnology: a promising method for oral cancer detection and diagnosis

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Xiao-Jie Chen ◽  
Xue-Qiong Zhang ◽  
Qi Liu ◽  
Jing Zhang ◽  
Gang Zhou
Keyword(s):  
Oral Cancer ◽  
Cancer Detection ◽  
Promising Method ◽  
Detection And Diagnosis

scholarly journals A Survey on Leukemia Detection using Image Processing Techniques

2021 ◽  
Vol 309 ◽  
pp. 01111
Author(s):  
Mohammed Junaid Ahmed ◽  
Padmalaya Nayak
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Research Papers ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Research Fields ◽  
Dataset Size ◽  
Detection And Diagnosis ◽  
Processing Techniques ◽  
Late Works

Leukemia detection and diagnosis by inspecting the blood cell images is an intriguing and dynamic exploration region in both the Artificial Intelligence and Medical research fields. There are numerous procedures created to look at blood tests to identify leukemia illness, these strategies are the customary methods and the deep learning (DL) strategy. This survey paper presents a review on the distinctive conventional strategies and Deep Learning and Machine Learning methods towards that have been utilized in leukemia illness diagnosis dependent on platelets images and to analyze between the two methodologies in nature of appraisal, exactness, cost and speed. This article covers 11 research papers, 9 of these examinations were in customary strategies which utilized image handling and AI (ML) calculations, such as, K-closest neighbor (KNN), K-means, SVM, Naïve Bayes, and 2 investigations in cutting edge procedures which utilized Deep Learning, especially Convolutional Neural Networks (CNNs) which is the most generally utilized in the field leukemia detection since it is profoundly precise, quick, and has the smallest expense. What's more, it dissects various late works that have been presented in the field including the dataset size, the pre-owned procedures, the acquired outcomes, and so forth. At last, in view of the led study, it very well may be reasoned that the proposed framework CNN was accomplishing immense triumphs in the field whether in regards to highlights extraction or classification time, precision and also a best low cost in the identification of leukemia.

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

scholarly journals Vision Atrophy Screening and Revelation

2021 ◽  
Author(s):  
Kiruthiga Devi M ◽  
Lingamuthu K ◽  
Baskar M ◽  
Deepa B ◽  
Merlin G
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Low Cost ◽  
Remote Diagnostics ◽  
Glaucoma Detection ◽  
Screening Services ◽  
Detection And Diagnosis ◽  
Retinal Fundus ◽  
To Receive

Glaucoma which is known as the “thief of sight”, is an irreversible eye disease It is mainly caused by increased intraocular pressure (IOP), or loss of blood supply to the optic nerve. Glaucoma detection and diagnosis is very important. By analyzing the optic disc and its surroundings, This paper introduces a method for providing automated glaucoma screening services based on a framework that proposes a retinal image synthesizer for glaucoma assessment by analyzing the optic disc and its surroundings. The Cup to Disc Ratio (CDR) is critical for the system, and it is calculated using 2-D retinal fundus images. The synthetic images produced by our system are compared quantitatively. The structural properties of synthetic and real images are analyzed, and the quality of colour is calculated by extracting the 2-D histogram. The system allows patients to receive low-cost remote diagnostics from a distance, preventing blindness and vision loss by early detection and management.

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