scholarly journals OVERVIEW OF EARLY DIAGNOSIS OF «DIABETES» BASED ON ARTIFICIAL INTELLIGENCE.

2021 ◽  
Vol 102 (10) ◽  
pp. 443-446
Author(s):  
D. T. Muhamadiyeva ◽  
◽  
X.A. Primova ◽  
S.S. Nabiyeva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Early Diagnosis Of Diabetes

PROSPECTS OF ARTIFICIAL INTELLIGENCE IN CARRYING OUT THE ONCOLOGICAL COMPONENT OF MEDICAL EXAMINATION OF THE POPULATION

2019 ◽  
Vol 65 (2) ◽  
pp. 234-237
Author(s):  
Vyacheslav Cherenkov ◽  
A. Petrov ◽  
I. Gulkov ◽  
A. Kostyukov
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Mass Screening ◽  
Malignant Tumors ◽  
Risk Groups ◽  
Modern World ◽  
Pilot Program ◽  
Urgent Problem ◽  
Gender And Age ◽  
Group Version

Diagnosis of malignant tumors is an urgent problem of the modern world. Early diagnosis depends on General practitioners. The doctor should conduct a systematic examination of the patient regularly, taking into account the risk groups, gender and age. With mass screening, signs of dysplasia or an early focus, developing cancer can «slip away» [1]. Optimization of analysis and examination algorithms is required, which is not always possible for one person. Positive application of the digital program with elements of imaging in Oncology, we were able to create such a class of tasks for the preliminary subjective-objective survey of patients in three versions: with a widescreen screen and consoles for patients (group version up to 15 or more patients), interactive (touch) and tablet. The results of the survey are sent through the accepted channels to the doctor with recommendations for further examination, and the patient is given a coupon. The pilot program showed that the system of such robotic technologies in the future can replace the oncologist in its development to artificial intelligence at the stage of the primary link.

AI-Provided Instant Differential Diagnosis of Pemphigus Vulgaris and Bullous Pemphigoid: Qualitative Study (Preprint)

2020 ◽  
Author(s):  
Xiaoyu He ◽  
Juan Su ◽  
Guangyu Wang ◽  
Kang Zhang ◽  
Navarini Alexander ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Image Classification ◽  
Bullous Pemphigoid ◽  
Skin Diseases ◽  
Pemphigus Vulgaris ◽  
Classification Model ◽  
Clinical Image ◽  
Clinical Images ◽  
Multimodal Classification

BACKGROUND Pemphigus vulgaris (PV) and bullous pemphigoid (BP) are two rare but severe inflammatory dermatoses. Due to the regional lack of trained dermatologists, many patients with these two diseases are misdiagnosed and therefore incorrectly treated. An artificial intelligence diagnosis framework would be highly adaptable for the early diagnosis of these two diseases. OBJECTIVE Design and evaluate an artificial intelligence diagnosis framework for PV and BP. METHODS The work was conducted on a dermatological dataset consisting of 17,735 clinical images and 346 patient metadata of bullous dermatoses. A two-stage diagnosis framework was designed, where the first stage trained a clinical image classification model to classify bullous dermatoses from five common skin diseases and normal skin and the second stage developed a multimodal classification model of clinical images and patient metadata to further differentiate PV and BP. RESULTS The clinical image classification model and the multimodal classification model achieved an area under the receiver operating characteristic curve (AUROC) of 0.998 and 0.942, respectively. On the independent test set of 20 PV and 20 BP cases, our multimodal classification model (sensitivity: 0.85, specificity: 0.95) performed better than the average of 27 junior dermatologists (sensitivity: 0.68, specificity: 0.78) and comparable to the average of 69 senior dermatologists (sensitivity: 0.80, specificity: 0.87). CONCLUSIONS Our diagnosis framework based on clinical images and patient metadata achieved expert-level identification of PV and BP, and is potential to be an effective tool for dermatologists in remote areas in the early diagnosis of these two diseases.

scholarly journals Artificial intelligence assisted electrocardiography for early diagnosis of thyrotoxic periodic paralysis

2021 ◽  
Author(s):  
Chin Lin ◽  
Chin-Sheng Lin ◽  
Ding-Jie Lee ◽  
Chia-Cheng Lee ◽  
Sy-Jou Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Validation Cohort ◽  
Laboratory Data ◽  
Diagnostic System ◽  
Periodic Paralysis ◽  
Medical Emergency ◽  
Diagnostic Process ◽  
Routine Laboratory ◽  
Thyrotoxic Periodic Paralysis

Abstract CONTEXT Thyrotoxic periodic paralysis (TPP) characterized by acute weakness, hypokalemia and hyperthyroidism is a medical emergency with a great challenge in early diagnosis since most TPP patients do not have overt symptoms. OBJECTIVE To assess artificial intelligence (AI)-assisted electrocardiography (ECG) combined with routine laboratory data in the early diagnosis of TPP. METHODS A deep learning model (DLM) based on ECG12Net, an 82-layer convolutional neural network, was constructed to detect hypokalemia and hyperthyroidism. The development cohort consisted of 39 ECGs from patients with TPP and 502 ECGs of hypokalemic control; the validation cohort consisted of 11 ECGs of TPP and 36 ECGs of non-TPP with weakness. The AI-ECG based TPP diagnostic process was then consecutively evaluated in 22 male patients with TTP-like features. RESULTS In the validation cohort, the DLM-based ECG system detected all cases of hypokalemia in TPP patients with a mean absolute error of 0.26 mEq/L and diagnosed TPP with an area under curve (AUC) of ~80%, surpassing the best standard ECG parameter (AUC=0.7285 for the QR interval). Combining the AI predictions with the estimated glomerular filtration rate (eGFR) and serum chloride (Cl -) boosted the diagnostic accuracy of the algorithm to AUC 0.986. In the prospective study, the integrated AI and routine laboratory diagnostic system had a PPV of 100% and F-measure 87.5%. CONCLUSIONS An AI-ECG system reliably identifies hypokalemia in patients with paralysis and integration with routine blood chemistries provides valuable decision support for the early diagnosis of TPP.

The application of artificial intelligence and radiomics in lung cancer

2020 ◽  
Vol 3 (3) ◽  
pp. 214-227
Author(s):  
Yaojie Zhou ◽  
Xiuyuan Xu ◽  
Lujia Song ◽  
Chengdi Wang ◽  
Jixiang Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Lung Cancer ◽  
Early Diagnosis ◽  
Medical Management ◽  
Causes Of Death ◽  
Current Application ◽  
The World ◽  
Great Progress ◽  
Made In

Abstract Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

scholarly journals Formation of a cooperation network in Mato Grosso on Machine Learning and Image Analysis: Diagnosis of COVID-19 in X-ray images

2021 ◽  
Author(s):  
Jeniffer Luz ◽  
Scenio De Araujo ◽  
Caio Abreu ◽  
Juvenal Silva Neto ◽  
Carlos Gulo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Early Diagnosis ◽  
Critical Mass ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mato Grosso ◽  
X Ray ◽  
Occurrence Matrix

Since the beginning of the COVID-19 outbreak, the scientific communityhas been making efforts in several areas, either by seekingvaccines or improving the early diagnosis of the disease to contributeto the fight against the SARS-CoV-2 virus. The use of X-rayimaging exams becomes an ally in early diagnosis and has been thesubject of research by the medical image processing and analysiscommunity. Although the diagnosis of diseases by image is a consolidatedresearch theme, the proposed approach aims to: a) applystate-of-the-art machine learning techniques in X-ray images forthe COVID-19 diagnosis; b) identify COVID-19 features in imagingexamination; c) to develop an Artificial Intelligence model toreduce the disease diagnosis time; in addition to demonstrating thepotential of the Artificial Intelligence area as an incentive for theformation of critical mass and encouraging research in machinelearning and processing and analysis of medical images in the Stateof Mato Grosso, in Brazil. Initial results were obtained from experimentscarried out with the SVM (Support Vector Machine) classifier,induced on a publicly available image dataset from Kaggle repository.Six attributes suggested by Haralick, calculated on the graylevel co-occurrence matrix, were used to represent the images. Theprediction model was able to achieve 82.5% accuracy in recognizingthe disease. The next stage of the studies includes the study of deeplearning models.

Sign in / Sign up

Export Citation Format

Share Document