"TRUE" INCIDENCE OF SKIN MELANOMA, BASED ON A MASSIVE 3-DAY EARLY DIAGNOSIS CAMPAIGN IN A BIG INDUSTRIAL CITY

2021 ◽  
pp. 71-83
Author(s):  
E.Yu. Neretin ◽  
S.Kh. Sadreeva
Keyword(s):  
Early Diagnosis ◽  
Incidence Rate ◽  
Large Scale ◽  
Multi Agent System ◽  
Skin Melanoma ◽  
True Incidence ◽  
Industrial City ◽  
Non Invasive ◽  
Official Rate ◽  
Multi Agent

Skin melanoma (SM) is a malignant tumor that is quite rarely diagnosed in Russia. However, both absolute and relative numbers (incidence) of patients with this diagnosis are growing. The trend persists for many years, but the official incidence rate does not reflect the true picture, so it is likely to be lower than the true one. The aim of the study was to calculate the assumed incidence of skin melanoma based on the data from a large-scale early diagnosis campaign. Materials and Methods. In 2019, 800 patients were examined during a 3-day campaign in the Samara region. A non-invasive diagnostic method (digital dermatoscopy), a multi-agent technology based on artificial intelligence and a proprietary technology (patent No. 2018620399, No. 2018613016) were used during the campaign. Four skin melanomas were identified at an early, pre-invasive stage. Results. Two different methods (depending on the percentage of population coverage and the campaign duration), made it possible to calculate a true indicator of the skin melanoma incidence. Conclusion. It was possible to adjust the "true" incidence rate of skin melanoma. In 2019, it ranged from 9.65 to 15.31 per 100 000 people, which is significantly higher than the official rate registered that year (8.11 per 100 000 population). Keywords: skin melanoma, true incidence, large-scale campaign, multi-agent system, skin melanoma modeling. Меланома кожи (МК) является злокачественной опухолью, которая встречается в РФ довольно редко, причем растет как абсолютное количество пациентов с данным диагнозом, так и относительное (заболеваемость). Данная тенденция стабильно сохраняется на протяжении многих лет, однако официальный показатель заболеваемости не отражает реальной картины и вполне вероятно, что он несколько ниже «истинного». Целью исследования был расчет предполагаемой заболеваемости меланомой кожи на основании данных, полученных в результате масштабной кампании по ранней диагностике. Материалы и методы. В Самарской области в 2019 г. была проведена 3-дневная кампания, которая позволила обследовать 800 обратившихся пациентов с помощью неинвазивного метода диагностики (цифровой дерматоскопии) и мультиагентной технологии, основанной на искусственном интеллекте и авторской методике (патент на изобретение № 2018620399, № 2018613016). Всего было выявлено 4 меланомы кожи на ранней, доинвазивной, стадии. Результаты. В ходе расчета по 2 различным методикам (в зависимости от процента охвата населения и продолжительности акции) был получен «истинный» показатель заболеваемости МК. Выводы. «Истинный» показатель заболеваемости МК был скорректирован и составил от 9,65 до 15,31 на 100 тыс. населения, что значительно больше официального, зарегистрированного в отчетном 2019 г. (8,11 на 100 тыс. населения). Ключевые слова: меланома кожи, «истинная» заболеваемость, масштабная кампания, мультиагентная система, моделирование заболеваемости меланомы кожи.

scholarly journals Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2992
Author(s):  
Niharika Singh ◽  
Irraivan Elamvazuthi ◽  
Perumal Nallagownden ◽  
Gobbi Ramasamy ◽  
Ajay Jangra
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Performance Metrics ◽  
Network Reliability ◽  
Operating Conditions ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Multi Agent ◽  
System Operating

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.

scholarly journals Multi-Agent System Supporting Automated Large-Scale Photometric Computations

Entropy ◽  
2016 ◽  
Vol 18 (3) ◽  
pp. 76 ◽  
Author(s):  
Adam Sȩdziwy ◽  
Leszek Kotulski
Keyword(s):  
Large Scale ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals Efficacy of Monocyte Distribution Width in the Early Diagnosis of Sepsis: A Diagnostic Meta-Analysis

2021 ◽  
Author(s):  
Jiahao Chen ◽  
Qiang Guo
Keyword(s):  
Early Diagnosis ◽  
Large Scale ◽  
Meta Analysis ◽  
Delayed Diagnosis ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Inexpensive Method ◽  
Diagnostic Efficacy ◽  
Distribution Width ◽  
Non Invasive

Abstract Background: Delayed diagnosis of sepsis urgently requires a fast, convenient, and inexpensive method to improve the early diagnosis of sepsis. Increasing evidence showed that monocyte distribution width (MDW) could be used as a non-invasive biomarker with high sensitivity and specificity for the early diagnosis of sepsis. However, the accuracy and reliability of its diagnosis are still controversial in different studies. Method: A meta-analysis of all available studies regarding the association between MDW and the diagnosis of sepsis was performed to systematically evaluate the diagnostic efficacy of MDW in the prediction of sepsis. Results: The estimated results of all eight studies are as follows: sensitivity, 0.84 (95% CI 0.77, 0.90); specificity, 0.68 (95% CI 0.54, 0.80); PLR, 2.7 (95% CI 1.8, 4.1); NLR, 0.23 (95% CI 0.15, 0.35); DOR is 12 (95% CI 5, 25). The corresponding overall area under the curve is 0.85 (95% CI 0.82, 0.88). Conclusion: In conclusion, this meta-analysis demonstrates that MDW has high accuracy in distinguishing patients with sepsis from healthy controls for early diagnosis of sepsis. However, large-scale prospective studies and joint diagnosis with other indicators are urgently required to confirm our findings and their utilization for routine clinical diagnosis in the future.

scholarly journals Effectiveness of multi-agent technology in three-day campaign for early diagnosis of cutaneous melanoma

2021 ◽  
Vol 9 (3) ◽  
pp. 433-446
Author(s):  
E.Yu. Neretin ◽  
◽  
S.Kh. Sadreyeva ◽  
◽  
Keyword(s):  
Early Diagnosis ◽  
Cutaneous Melanoma ◽  
Large Scale ◽  
Early Stage ◽  
Good Prognosis ◽  
Agent Technology ◽  
Correct Treatment ◽  
Scale Population ◽  
Multi Agent ◽  
Epidemiological Situation

BACKGROUND: Melanoma — is a tumor that in most cases affects the skin and is characterized by an extremely aggressive course and a steadily increasing morbidity in the world. However, diagnosed at an early stage, cutaneous melanoma (CM) has a good prognosis with correct treatment. The results of the diagnosis of CM can be improved by joint the efforts of dermatologists and artificial intelligence (AI). AIM: Cutaneous melanoma is a tumor with an unpredictable course. The article discusses ways to solve the problem of early diagnosis using multi-agent technology and an expert system based on AI. MATERIALS AND METHODS: In a large industrial city with more than three million population, a three-day campaign for the early diagnostics of cutaneous melanoma was carried out, which revealed 4 cases of CM (4.35%) at pT1a stage in 96 patients registered in 2019. A total of 800 people were examined. RESULTS: As a result of diagnostics, the following data were obtained: specificity of self-diagnostics of the region was 6.78% by the inhabitants, 78.89% by dermatologists, and 95.24% by expert oncologists. In prospective quality control of diagnostics within 6 months, such parameters as the sensitivity of diagnosing cutaneous melanoma by oncologists and dermatologists were both 100%. As a result of the study, it was found that the multi-agent technology is necessary to improve the results of CM diagnostics, and also for a more complete assessment of the onco-epidemiological situation, and for forecasting of the necessary resources in the region. CONCLUSIONS: The multi-agent technology can improve diagnostic results, but for a more complete assessment of the onco-epidemiological situation, a large-scale population screening in the region is required.

scholarly journals Methods of Protecting the Distributed Computation in Multi-Agent System

2018 ◽  
Vol 7 (3.13) ◽  
pp. 38
Author(s):  
S A. Khovanskov ◽  
K E. Rumyantsev ◽  
V S. Khovanskova
Keyword(s):  
Distributed Computing ◽  
Large Scale ◽  
Computer Network ◽  
Multi Agent System ◽  
Agent System ◽  
Distributed Computing Systems ◽  
Modeling Process ◽  
Distributed Computations ◽  
False Result ◽  
Multi Agent

Currently, there are many different approaches for organization of the distributed calculations in computer network technology grid, metacomputing (BOINC, PVM, and others).  The main drawback of most existing approaches is that they are designed to create centralized distributed computing systems. In this article we propose to organize the solution of such problems as multivariate modeling, through the creation of distributed computations in computer networks based on decentralized multi-agent system. When used as a computing environment a computer network on a large scale can cause threats to the security of distributed computing from the intruders. One of these threats is getting the calculation about the result by the attacker. A false result can leads in the modeling process to adopt is not optimal or wrong decisions. We developed a method of protecting distributed computing from the threat of receiving false result.  

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