scholarly journals Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain

10.2196/13594 ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. e13594 ◽  
Author(s):  
Xiao-Su Hu ◽  
Thiago D Nascimento ◽  
Mary C Bender ◽  
Theodore Hall ◽  
Sean Petty ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Real Time ◽  
Pain Detection ◽  
Detection And Localization ◽  
The Brain

scholarly journals Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain (Preprint)

2019 ◽  
Author(s):  
Xiao-Su Hu ◽  
Thiago D. Nascimento ◽  
Mary C Bender ◽  
Theodore Hall ◽  
Sean Petty ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Real Time ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Positive Likelihood Ratio ◽  
Cortical Activity ◽  
Functional Near Infrared Spectroscopy ◽  
Pain Detection ◽  
The Brain

BACKGROUND For many years, clinicians have been seeking for objective pain assessment solutions via neuroimaging techniques, focusing on the brain to detect human pain. Unfortunately, most of those techniques are not applicable in the clinical environment or lack accuracy. OBJECTIVE This study aimed to test the feasibility of a mobile neuroimaging-based clinical augmented reality (AR) and artificial intelligence (AI) framework, CLARAi, for objective pain detection and also localization direct from the patient’s brain in real time. METHODS Clinical dental pain was triggered in 21 patients by hypersensitive tooth stimulation with 20 consecutive descending cold stimulations (32°C-0°C). We used a portable optical neuroimaging technology, functional near-infrared spectroscopy, to gauge their cortical activity during evoked acute clinical pain. The data were decoded using a neural network (NN)–based AI algorithm to classify hemodynamic response data into pain and no-pain brain states in real time. We tested the performance of several networks (NN with 7 layers, 6 layers, 5 layers, 3 layers, recurrent NN, and long short-term memory network) upon reorganized data features on pain diction and localization in a simulated real-time environment. In addition, we also tested the feasibility of transmitting the neuroimaging data to an AR device, HoloLens, in the same simulated environment, allowing visualization of the ongoing cortical activity on a 3-dimensional brain template virtually plotted on the patients’ head during clinical consult. RESULTS The artificial neutral network (3-layer NN) achieved an optimal classification accuracy at 80.37% (126,000/156,680) for pain and no pain discrimination, with positive likelihood ratio (PLR) at 2.35. We further explored a 3-class localization task of left/right side pain and no-pain states, and convolutional NN-6 (6-layer NN) achieved highest classification accuracy at 74.23% (1040/1401) with PLR at 2.02. CONCLUSIONS Additional studies are needed to optimize and validate our prototype CLARAi framework for other pains and neurologic disorders. However, we presented an innovative and feasible neuroimaging-based AR/AI concept that can potentially transform the human brain into an objective target to visualize and precisely measure and localize pain in real time where it is most needed: in the doctor’s office. INTERNATIONAL REGISTERED REPOR RR1-10.2196/13594

scholarly journals Contribution of Cognitive Science and Artificial Intelligence in the Simulation of the Complex Human Mind

2020 ◽  
Vol 9 (2) ◽  
pp. 928-932
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Real Time ◽  
Human Mind ◽  
Scientific Study ◽  
Computational Procedures ◽  
The Mind ◽  
And Behavior ◽  
Interdisciplinary Theory ◽  
The Brain

The research incorporated encircles the interdisciplinary theory of cognitive science in the branch of artificial intelligence. It has always been the end goal that better understanding of the idea can be guaranteed. Besides, a portion of the real-time uses of cognitive science artificial intelligence have been taken into consideration as the establishment for more enhancements. Before going into the scopes of future, there are many complexities that occur in real-time which have been uncovered. Cognitive science is the interdisciplinary, scientific study of the brain and its procedures. It inspects the nature, the activities, and the elements of cognition. Cognitive researchers study intelligence and behavior, with an emphasis on how sensory systems speak to, process, and change data. Intellectual capacities of concern to cognitive researchers incorporate recognition, language, memory, alertness, thinking, and feeling; to comprehend these resources, cognitive researchers acquire from fields, for example, psychology, artificial intelligence, philosophy, neuroscience, semantics, and anthropology. The analytic study of cognitive science ranges numerous degrees of association, from learning and choice to logic and planning; from neural hardware to modular mind organization. The crucial idea of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

scholarly journals A Theoretical Concept to Increase the Trustworthiness of Online and Offline Debates with Real-Time AI Speech Analytics

2021 ◽  
Author(s):  
Kevin Koidl
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Real Time ◽  
Theoretical Concept ◽  
Critical Component ◽  
Reliable Communication ◽  
Democratic Institution ◽  
Key Indicator ◽  
Technological Advances ◽  
Augmented Reality Technology

Debates are an essential democratic institution in danger by the rise of Social Media. The advent of Fake News often referred to as the ‘crisis of trust’, has led to a substantial increase in debates that blend online and offline. It can be argued that blended approaches are not directly linked to increasing trustworthiness in the debate. To overcome this trust crisis and increase the reliability in debates, we introduce the HELIOSPHERE concept that seeks to use technological advances, such as Artificial Intelligence and Augmented Reality, to create a more fair, inclusive and transparent debate. The critical component for inclusiveness is Augmented Reality technology and 3D camera technology to hybridise the online and offline debating space and ensure that anyone who cannot be present can engage with the debate. For transparency and fairness, a key indicator of trust, an Artificial Intelligence dashboard is introduced to analyse and visualise speaking time, speaker gender, topic relatedness, bias detection sentiment in Real-Time. This work presents the overall theoretical concept focusing on academic and technical concepts to support reliable communication within debates.

scholarly journals An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

2019 ◽  
Vol 25 (9) ◽  
pp. 1453-1457 ◽  
Author(s):  
Po-Hsuan Cameron Chen ◽  
Krishna Gadepalli ◽  
Robert MacDonald ◽  
Yun Liu ◽  
Shiro Kadowaki ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Real Time ◽  
Cancer Diagnosis

scholarly journals Investigasi Pengaruh Step Training pada Metode Single Shot Multibox Detector untuk Marker dalam Teknologi Augmented Reality

2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Vivian Alfionita Sutama ◽  
Suryo Adhi Wibowo ◽  
Rissa Rahmania
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Augmented Reality ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Rate ◽  
Batch Size ◽  
Single Shot

Nowadays, Artificial Intelligence is one of the most developing technology, especially on Augmented Reality (AR). AR is a technology which connected between real world and virtual in a real time that allows user to interact directly and display it in 3D. AR technology has two methods, that are AR based on marker and AR based on markerless. However, AR based on marker need an object detection system which has high performance as an interaction tools between user and the device. Single shot multibox detector (SSD) is an object detection algorithm that has fast learning computation and good performance. This method is affected by some parameters like number of epoch, learning rate, batch size, step training, etc. However, to create a good system it took a long process such as taking dataset, labelling process, then training and testing models to gain the best performance. In this experiment, we analyze SSD method in AR technology using inception architecture as pre-trained Convolutional neural network (CNN), and then do transfer learning to minimize amount training time. The configuration that used is the number of step training. The result of this experiment gets the best accuracy in 70.17%. Then, the best performance is used as an object detection model for marker’s AR technology.Abstrak Saat ini, Artificial intelligence merupakan teknologi yang sedang berkembang pesat. Salah satunya adalah teknologi Augmented Reality (AR). AR adalah teknologi yang menggabungkan dunia nyata dengan virtual secara real-time dengan interaksi pengguna secara langsung dan menampilkannya dalam bentuk 3D. Teknologi AR ini memiliki dua metode yaitu dengan marker dan markerless. Dalam perkembangannya, AR berbasis marker membutuhkan sistem deteksi objek yang memiliki performa tinggi sebagai alat interaksi antara pengguna dengan perangkatnya. Single shot multibox detector (SSD) merupakan algoritma deteksi objek yang memiliki komputasi pembelajaran dan kinerja yang baik. Metode ini dipengaruhi oleh beberapa parameter seperti jumlah lapisan konvolusi, epoch, learning rate, jumlah batch, step training, dll. Namun, dalam mengimplementasikannya diperlukan proses yang cukup panjang seperti, pengambilan dataset, proses pelabelan, proses pelatihan menggunakan metode SSD, dan melakukan pengujian terhadap beberapa model untuk mencari perfomansi paling baik. Dalam percobaan ini, kami melakukan analisis terhadap metode SSD pada teknologi AR menggunakan arsitektur Inception sebagai pre-trained Convolutional neural network (CNN), kemudian dilakukan transfer learning untuk memperkecil jumlah kelas data pelatihan dan waktu pelatihan data. Konfigurasi yang digunakan berupa jumlah step pada pelatihan. Hasil dari penilitian ini menunjukan akurasi terbaik sebesar 70,17%. Kemudian, perfomansi terbaik digunakan sebagai model deteksi objek untuk marker pada teknologi AR.

scholarly journals Recent Advancements of Augmented Reality in Real Time Applications

2019 ◽  
Vol 8 (2S7) ◽  
pp. 537-542
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Real Time ◽  
Advanced Technology ◽  
Virtual Objects ◽  
Digital World ◽  
Real Time Applications ◽  
Natural Way

Augmented Reality (AR) is an advanced technology that improves users view of interactivity with the present reality by adding virtual objects in it and it is the most natural way to interface with your digital world. This paper presents a survey about real-time applications, commonly used technologies, various open challenges, possible set of solution provided by several researchers and academicians. This paper also provides future of Augmented reality in various areas of artificial intelligence.

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