scholarly journals Artificial intelligence facilitates decision-making in the treatment of lumbar disc herniations

2020 ◽  
Author(s):  
André Wirries ◽  
Florian Geiger ◽  
Ahmed Hammad ◽  
Ludwig Oberkircher ◽  
Ingmar Blümcke ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Deep Learning ◽  
Conservative Therapy ◽  
Early Stage ◽  
Lumbar Disc ◽  
Accurate Prediction ◽  
Neurological Deficits ◽  
Absolute Difference ◽  
Disc Herniations

Abstract Purpose Apart from patients with severe neurological deficits, it is not clear whether surgical or conservative treatment of lumbar disc herniations is superior for the individual patient. We investigated whether deep learning techniques can predict the outcome of patients with lumbar disc herniation after 6 months of treatment. Methods The data of 60 patients were used to train and test a deep learning algorithm with the aim to achieve an accurate prediction of the ODI 6 months after surgery or the start of conservative therapy. We developed an algorithm that predicts the ODI of 6 randomly selected test patients in tenfold cross-validation. Results A 100% accurate prediction of an ODI range could be achieved by dividing the ODI scale into 12% sections. A maximum absolute difference of only 3.4% between individually predicted and actual ODI after 6 months of a given therapy was achieved with our most powerful model. The application of artificial intelligence as shown in this work also allowed to compare the actual patient values after 6 months with the prediction for the alternative therapy, showing deviations up to 18.8%. Conclusion Deep learning in the supervised form applied here can identify patients at an early stage who would benefit from conservative therapy, and on the contrary avoid painful and unnecessary delays for patients who would profit from surgical therapy. In addition, this approach can be used in many other areas of medicine as an effective tool for decision-making when choosing between opposing treatment options, despite small patient groups.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Danju Huang ◽  
Han Bai ◽  
Li Wang ◽  
Yu Hou ◽  
Lan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Radiation Oncology ◽  
Clinical Work ◽  
Radiation Oncologists ◽  
Basic Concepts ◽  
Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

The Black Box Myth

2019 ◽  
Vol 1 (1) ◽  
pp. 1-3
Author(s):  
Abraham Rudnick
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Deep Learning ◽  
Black Box ◽  
Dynamic Decision Making ◽  
Empirical Knowledge ◽  
Negative Consequences ◽  
Extreme Caution ◽  
Scientific Facts

Artificial intelligence (AI) and its correlates, such as machine and deep learning, are changing health care, where complex matters such as comoribidity call for dynamic decision-making. Yet, some people argue for extreme caution, referring to AI and its correlates as a black box. This brief article uses philosophy and science to address the black box argument about knowledge as a myth, concluding that this argument is misleading as it ignores a fundamental tenet of science, i.e., that no empirical knowledge is certain, and that scientific facts – as well as methods – often change. Instead, control of the technology of AI and its correlates has to be addressed to mitigate such unexpected negative consequences.

Limits of trust in medical AI

2020 ◽  
Vol 46 (7) ◽  
pp. 478-481 ◽  
Author(s):  
Joshua James Hatherley
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Clinical Practice ◽  
Medical Decision Making ◽  
Hospital Readmissions ◽  
New Drugs ◽  
Medical Decision ◽  
Practice Of Medicine

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.

Anterior Lumbar Microdiscectomy and Interbody Fusion for the Treatment of Recurrent Disc Herniation

Neurosurgery ◽  
2001 ◽  
Vol 48 (2) ◽  
pp. 334-338 ◽  
Author(s):  
A. Giancarlo Vishteh ◽  
Curtis A. Dickman
Keyword(s):  
Interbody Fusion ◽  
Lumbar Disc ◽  
Posterior Longitudinal Ligament ◽  
Radicular Pain ◽  
Neurological Deficits ◽  
Herniated Disc ◽  
X Rays ◽  
Lumbar Microdiscectomy ◽  
Disc Herniations

Abstract OBJECTIVE To demonstrate the feasibility of anterior lumbar microdiscectomy in patients with recurrent, sequestered lumbar disc herniations. METHODS Between 1997 and 1999, six patients underwent a muscle-sparing “minilaparotomy” approach and subsequent microscopic anterior lumbar microdiscectomy and fragmentectomy for recurrent lumbar disc extrusions at L5–S1 (n = 4) or L4–L5 (n = 2). A contralateral distraction plug permitted ipsilateral discectomy under microscopic magnification. Effective resection of the extruded disc fragments was accomplished by opening the posterior longitudinal ligament. Interbody fusion was performed by placing cylindrical threaded titanium cages (n = 4) or threaded allograft bone dowels (n = 2). RESULTS There were no complications, and blood loss was minimal. Follow-up magnetic resonance imaging revealed complete resection of all herniated disc material. Plain x-rays revealed excellent interbody cage position. Radicular pain and neurological deficits resolved in all six patients (mean follow-up, 14 mo). CONCLUSION Anterior lumbar microdiscectomy with interbody fusion provides a viable alternative for the treatment of recurrent lumbar disc herniations. Recurrent herniated disc fragments can be removed completely under direct microscopic visualization, and interbody fusion can be performed in the same setting.

A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

2021 ◽  
pp. 318-339
Author(s):  
Mehmet Ali Şimşek ◽  
Zeynep Orman
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Deep Learning ◽  
Industry 4.0 ◽  
Production Efficiency ◽  
Learning Technologies ◽  
High Potential ◽  
Data Sets ◽  
Learning Methods ◽  
Intelligence Studies

Nowadays, the main features of Industry 4.0 are interpreted to the ability of machines to communicate with each other and with a system, increasing the production efficiency and development of the decision-making mechanisms of robots. In these cases, new analytical algorithms of Industry 4.0 are needed. By using deep learning technologies, various industrial challenging problems in Industry 4.0 can be solved. Deep learning provides algorithms that can give better results on datasets owing to hidden layers. In this chapter, deep learning methods used in Industry 4.0 are examined and explained. In addition, data sets, metrics, methods, and tools used in the previous studies are explained. This study can lead to artificial intelligence studies with high potential to accelerate the implementation of Industry 4.0. Therefore, the authors believe that it will be very useful for researchers and practitioners who want to do research on this topic.

scholarly journals Spontaneous and rapid resolution of a massive lumbar disc herniation

2021 ◽  
Vol 12 ◽  
pp. 352
Author(s):  
Dinesh Naidoo
Keyword(s):  
Lumbar Disc Herniation ◽  
Disc Herniation ◽  
Spontaneous Recovery ◽  
Surgical Intervention ◽  
Nonoperative Management ◽  
Lumbar Disc ◽  
Cauda Equina ◽  
Neurological Deficits ◽  
Lumbar Surgery ◽  
Disc Herniations

Background: Most lumbar disc herniations can be successfully treated conservatively. However, massive lumbar disc herniations are often treated surgically to avoid permanent cauda equina syndromes/neurological deficits and potential litigation. Nevertheless, here, we present a 51-year-old female who refused lumbar surgery due to coronavirus disease 2019 (COVID-19) and sustained a full spontaneous recovery without surgical intervention. Case Description: A 51-year-old female presented with a massive lumbar disc herniation at the L5S1 level. Despite refusing surgery for fear of getting COVID-19, she spontaneously neurologically improved without any residual neurological or radiographic sequelae. Conclusion: Although the vast majority of patients with massive lumbar disc herniations are managed surgically, there are rare instances in which nonoperative management may be successful.

scholarly journals Machine learning in medicine: what clinicians should know

2021 ◽  
Author(s):  
JZT Sim ◽  
QW Fong ◽  
WM Huang ◽  
CH Tan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Decision Making Process ◽  
Application Domain ◽  
Basic Concepts ◽  
Potential Impact

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician’s decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.

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