scholarly journals Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaokun Wang ◽  
Shan Huang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Elderly Patients ◽  
Cognitive Dysfunction ◽  
Recovery Time ◽  
Postoperative Cognitive Dysfunction ◽  
General Anesthetics ◽  
Extubation Time ◽  
Preventive Medication ◽  
The Impact

Postoperative cognitive dysfunction (POCD) refers to the complications of the central nervous system before and after surgery in patients without mental disorders. Many studies have shown that surgical anesthesia may cause POCD, especially in elderly patients. This article aims to study the relationship between artificial intelligence-based general anesthetics and postoperative cognitive dysfunction. This article first describes and classifies artificial intelligence, introduces its realization method, machine learning algorithms, and briefly introduces the basic principles of regression and classification methods in machine learning; then, the principles and techniques of general anesthetics are proposed. The pathogenesis of postoperative cognitive dysfunction (POCD) is explained in detail. Finally, the effect of anesthetics on postoperative cognitive dysfunction is obtained from both inhaled anesthetics and intravenous anesthetics. The impact on postoperative cognitive function is explained. The experimental results in this article show that there is no statistically significant difference in the two groups of patients’ age, gender ratio, body mass index, education level, preoperative comorbidities, and other general indicators. Through the use of EEG bispectral index monitors to monitor the depth of anesthesia and postoperative cognitive dysfunction, first, there was no obvious relationship between the occurrence of postoperative cognitive dysfunction at 1, 5, 10, and 50 days and discharge time. The comprehensive monitoring group can reduce the clinical dose of preventive medication and cis-atracurium and shorten the patient’s recovery time, extubation time, and recovery time. In addition, it can also reduce the increase of serum protein S100β in elderly patients and reduce the incidence of early postoperative cognitive dysfunction.

scholarly journals Effect of General Versus Spinal Anesthesia on Postoperative Delirium and Early Cognitive Dysfunction in Elderly Patients

2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Roghayeh Ehsani ◽  
Soudabeh Djalali Motlagh ◽  
Behrooz Zaman ◽  
Saloumeh Sehat Kashani ◽  
Mohammad Reza Ghodraty
Keyword(s):  
Elderly Patients ◽  
Spinal Anesthesia ◽  
Cognitive Dysfunction ◽  
Postoperative Cognitive Dysfunction ◽  
The Elderly ◽  
Academic Hospitals ◽  
The Impact ◽  
To Receive ◽  
State Examination ◽  
Trial Setting

Background: Postoperative cognitive dysfunction (POCD) and delirium are common in the elderly patients, given the controversial results of previous studies about the impact of anesthesia type on the occurrence of these complications. Objectives: This study was planned to compare the effects of general and spinal anesthesia on the prevalence of POCD and delirium. Methods: A single-blind non-randomized clinical trial. Setting was in two academic hospitals. Ninety-four patients over 50 years old scheduled for hip fracture fixation. Patients were divided into two groups to receive either general (GA) or spinal (SA) anesthesia. Both Mini-Mental State examination (MMSE) and Wechsler tests were used before the operation and 3 times postoperatively to assess the cognitive function and detect early POCD. The DSM-IV criteria were also used for the diagnosis of delirium. The incidence of delirium and POCD and their precipitating factors were compared between the two groups. Results: Ninety-four patients with a mean age of 67.12 years were studied. The overall prevalence of POCD and delirium was 17.02%; however, it was significantly higher in the GA group rather than the SA group, 29.7%, and 4.25%, respectively (P < 0.001). There was a significant relationship between age (P = 0.048), ASA class (P = 0.034), and educational level with the incidence of POCD, meaning that the probability of developing cognitive impairment decreases with patients’ higher level of education and lower ASA-physical status. Also, the rate of POCD in men was significantly higher than in women (P = 0.026). Conclusions: The finding of this study showed that, if there is no specific contraindication, neuraxial anesthesia may be preferred over general anesthesia in elderly patients.

scholarly journals Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey

2020 ◽  
Vol 6 ◽  
pp. 205520762096835
Author(s):  
C Blease ◽  
C Locher ◽  
M Leon-Carlyle ◽  
M Doraiswamy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Medical Profession ◽  
Descriptive Analysis ◽  
Psychiatric Practice ◽  
Limited Attention ◽  
Written Responses ◽  
Future Technology ◽  
Qualitative Descriptive ◽  
The Impact

Background The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. Objective This study aimed to explore psychiatrists’ opinions about the potential impact innovations in artificial intelligence and machine learning on psychiatric practice Methods In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written responses (“comments”) to three open-ended questions in the survey. Results Comments were classified into four major categories in relation to the impact of future technology on: (1) patient-psychiatrist interactions; (2) the quality of patient medical care; (3) the profession of psychiatry; and (4) health systems. Overwhelmingly, psychiatrists were skeptical that technology could replace human empathy. Many predicted that ‘man and machine’ would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. Conclusions This study presents timely information on psychiatrists’ views about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals Early Adopters to Early Majority - What's Driving the Artificial Intelligence and Machine Learning Powered Transformation in Financial Services?

2021 ◽  
Vol 12 (4) ◽  
pp. 43
Author(s):  
Srikrishna Chintalapati
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Portfolio Management ◽  
Value Chain ◽  
Financial Services ◽  
Retail Banking ◽  
Early Adopters ◽  
Customer Life Cycle ◽  
Digital Disruption ◽  
The Impact

From retail banking to corporate banking, from property and casualty to personal lines, and from portfolio management to trade processing, the next wave of digital disruption in financial services has been unleashed by the concepts and applications of Artificial Intelligence (AI) and Machine Learning (ML). Together, AI and ML are undoubtedly creating one of the largest technological transformations the world has ever witnessed. Within the advanced streams of research in AI and ML, human intelligence blended with the cognitive reasoning of machines is finally out of the labs and into real-time applications. The Financial Services sector is one of the early adopters of this revolution and arguably much ahead of its leverage compared to other sectors. Built on the conceptual foundations of Innovation diffusion, and a contemporary perspective of enterprise customer life-cycle journey across the AI-value chain defined by McKinsey Global Institute (2017), the current study attempts to highlight the features and use-cases of early-adopters of this transformation. With the theoretical underpinning of technology adoption lifecycle, this paper is an earnest attempt to comment on how AI and ML have been significantly transforming the Financial Services market space from the lens of a domain practitioner. The findings of this study would be of particular relevance to the subject matter experts, Industry analysts, academicians, and researchers focussed on studying the impact of AI and ML in the financial services industry.

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

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