scholarly journals O9‐3: Yoshiaki Zaizen 1 , Yuki Kanahori 2 , Yuka Kitamura 2 , Han‐Seung Yoon 2 , Andrey Bychkov 3 , Mutsumi Ozasa 2 , Hiroshi Mukae 4 , Tomoaki Hoshino 1 , Junya Fukuoka 2 Artificial intelligence‐assisted pathological screening of acid‐fast bacilli may be useful for early detection of mycobacteriosis:

Respirology ◽  
2021 ◽  
Vol 26 (S3) ◽  
pp. 26-26
Keyword(s):  
Artificial Intelligence ◽  
Early Detection ◽  
Acid Fast Bacilli

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.

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis

2020 ◽  
Vol 48 (11) ◽  
pp. e1091-e1096
Author(s):  
Meicheng Yang ◽  
Chengyu Liu ◽  
Xingyao Wang ◽  
Yuwen Li ◽  
Hongxiang Gao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Detection ◽  
Explainable Artificial Intelligence

scholarly journals Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

2021 ◽  
Vol 27 (38) ◽  
pp. 6399-6414
Author(s):  
Michelle Viscaino ◽  
Javier Torres Bustos ◽  
Pablo Muñoz ◽  
Cecilia Auat Cheein ◽  
Fernando Auat Cheein
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Early Detection ◽  
Comprehensive Review

scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

scholarly journals Application of Artificial Intelligence in the Prevention, Diagnosis and Treatment of Alzheimer’s Disease: New Hope for Dealing with Aging in China

2021 ◽  
Author(s):  
Hangtian Wang ◽  
Guofu Wang
Keyword(s):  
Artificial Intelligence ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Treatment Outcomes ◽  
Recent Progress ◽  
Diagnosis And Treatment ◽  
Cochrane Library ◽  
Poor Treatment ◽  
The Cochrane Library

Alzheimer’s disease (AD) has become a major issue around world, including China. The two major challenges for AD are the difficulty in early detection and poor treatment outcomes. Over the past decades, artificial intelligence (AI) was more and more widely used in the prevention, diagnosis and treatment of AD, which might be helpful to deal with the aging of population in China. Here, after a systematic literature searching on three English databases (MEDLINE, EMBASE, the Cochrane library), we briefly reviewed recent progress on the utilization of AI in the susceptibility analysis, diagnosis and management of AD. However, it is still in its infancy. More researches should be performed to improve the prognosis of patients with AD in the future.

Assess A Smartphone App (AICaries) that uses artificial intelligence to detect dental caries in children and provide interactive oral health education: Protocol for design and usability testing (Preprint)

2021 ◽  
Author(s):  
Jin Xiao ◽  
Jiebo Luo ◽  
Oriana Ly-Mapes ◽  
Tong Tong Wu ◽  
Timothy Dye ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Dental Caries ◽  
Early Detection ◽  
Young Children ◽  
Low Income ◽  
Fine Tuning ◽  
Caries Detection ◽  
Smartphone App ◽  
Caries Risk

BACKGROUND Early childhood caries (ECC) is the most common chronic childhood disease, with nearly 1.8 billion new cases per year globally. ECC afflicts approximately 55% of low-income and minority US preschool children, resulting in harmful short- and long-term effects on health and quality of life. Clinical evidence shows that caries is reversible if detected and addressed in its early stages. However, many low-income US children often have poor access to pediatric dental services. In this underserved group, dental caries is often diagnosed at a late stage when extensive restorative treatment is needed. With more than 85% of lower-income Americans owning a smartphone, mHealth tools, such as smartphone application, hold great promise to achieve patient-driven early detection and risk control of ECC. OBJECTIVE This study aims to employ a community-based participatory research strategy to refine and test the usability of an artificial intelligence (AI) -powered smartphone app, AICaries, to be used by children's parents/caregivers for dental caries detection in their children. METHODS Our previous work has led to the prototype of AICaries, which offers AI-powered caries detection using photos of children's teeth taken by the parents' smartphones, interactive caries risk assessment, and personalized education on reducing children's ECC risk. This AICaries study will utilize a 2-step qualitative study design to assess the feedback and usability of the app component, app flow and whether parents can take photo of children’s teeth on their own. Specifically, in Step 1, we will conduct individual usability tests among 10 pairs of end-users (parents with young children) to facilitate app module modification and fine-tuning using Think-aloud and Instant Data Analysis strategies. In Step 2, we will conduct unmoderated field testing for app feasibility and acceptability among 32 pairs of parents with their young children to assess the usability and acceptability of AICaries, including assessing the number/quality of teeth images taken by the parents for their children and parents’ satisfaction. RESULTS The study is funded by the National Institute of Dental and Craniofacial Research, USA. This study received IRB approval and launched in August, 2021. Data collection and analysis are expected to conclude by March 2021 and June 2022, respectively. CONCLUSIONS Using AICaries, parents can use their regular smartphones to take photo of their children’s teeth and detect ECC aided by AICaries, so that they can actively seek treatment for their children at an early and reversible stage of ECC. Using AICaries, parents can also obtain essential knowledge on reducing their children's caries risk. Data from this study will support future clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children.

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