scholarly journals Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses

2022 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Haseeb Sultan ◽  
Muhammad Owais ◽  
Jiho Choi ◽  
Tahir Mahmood ◽  
Adnan Haider ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Early Recognition ◽  
Patient Specific ◽  
Medical Procedure ◽  
Convolutional Network ◽  
Data Set ◽  
X Ray ◽  
Perioperative Morbidity ◽  
Public Data

Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.

scholarly journals Artificial intelligence in arthroplasty

Arthroplasty ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Glen Purnomo ◽  
Seng-Jin Yeo ◽  
Ming Han Lincoln Liow
Keyword(s):  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Recent Literature ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Patient Specific ◽  
Allocation Decision ◽  
Medical Specialties ◽  
Future Direction ◽  
Improve Patient

AbstractArtificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

scholarly journals Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

2021 ◽  
Author(s):  
Holger Roth ◽  
Ziyue Xu ◽  
Carlos Tor Diez ◽  
Ramon Sanchez Jacob ◽  
Jonathan Zember ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
State Of The Art ◽  
Automatic Detection ◽  
Patient Specific ◽  
Lesion Segmentation ◽  
Chest Computed Tomography ◽  
Public Data ◽  
Lung Ct ◽  
Detection And Quantification

Abstract Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

scholarly journals Deep convolutional approaches for the analysis of Covid-19 using chest X-Ray images from portable devices

2020 ◽  
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Milena Cruz ◽  
Laura Abelairas ◽  
...  
Keyword(s):  
Network Architecture ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
World Health ◽  
Portable Devices ◽  
Learning Approaches ◽  
Portable Equipment ◽  
Convolutional Network ◽  
X Ray ◽  
Chest X Ray

The recent human coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared as a global pandemic on 11 March 2020 by the World Health Organization. Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role for the screening, early detection and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-Ray images due to its accessibility, widespread availability and benefits regarding to infection control issues, minimizing the risk of cross contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-Ray images acquired by portable equipment into 3 different clinical categories: normal, pathological and COVID-19. For this purpose, two complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of both approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset provided by the Radiology Service of the Complexo Hospitalario Universitario A Coruña (CHUAC) specifically retrieved for this research. Despite the poor quality of chest X-Ray images that is inherent to the nature of the portable equipment, the proposed approaches provided satisfactory results, allowing a reliable analysis of portable radiographs, to support the clinical decision-making process.

scholarly journals Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Zeeshan Ahmed ◽  
Khalid Mohamed ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Precision Medicine ◽  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Laboratory ◽  
Social Issues ◽  
Patient Specific ◽  
Learning Platform

Abstract Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

scholarly journals A Review of Current Applications of Artificial Intelligence and Machine Learning in Medical Science

2021 ◽  
pp. 316-325
Author(s):  
Jayant Kumar A Rathod ◽  
Naveen Bhavani ◽  
Prenita Prinsal Saldanha ◽  
Preethi M Rao ◽  
Prasad Patil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Medical Science ◽  
Clinical Decision ◽  
Medical Sciences ◽  
Data Set ◽  
Time Problem ◽  
The Impact

Artificial Intelligence and Machine Learning are two fields that are causing substantial development in every field specifically in the field of medical sciences; for the stupendous potential that it can provide to assist the clinicians, researchers, in clinical decision making, automate time consuming procedures, medical imaging, and more. Most implementations of AI/ML rely on static data set, and this where the big data steps in. That is, these models are developed and trained on a data set that is already recorded and have been diligently reviewed for accuracy; leading to a precise decision-making process. Experts foresee that AI/ML based overarching care system will develop high-quality patient care and innovative research, aiding advanced decision support tools. In this paper we shall realize what are the current devices that are build and are being used for real time problem solving, also discuss the impact of Software as a Medical Device (SAMD) in future of medical sciences. [2,3,11]

scholarly journals Dangerous goods identification based on multi-channel neural network

2021 ◽  
Vol 2082 (1) ◽  
pp. 012008
Author(s):  
XiaoTian Wei ◽  
ZiQiang Hao ◽  
Bo Du
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Artificial Eye ◽  
Data Set ◽  
X Ray ◽  
Dangerous Goods ◽  
Public Data ◽  
Increasing Demand ◽  
Security Inspection ◽  
Eye Recognition

Abstract In the current society, there is an increasing demand for dangerous goods identification technology in X-ray images, but at the current stage, most of the identification of dangerous goods in X-ray images still relies on artificial eye recognition. In order to solve this problem, this paper proposes A method for automatically and intelligently identifying dangerous goods in X-ray images based on the transformation of the convolutional neural network. By adding multi-channel convolution and normalization to the convolutional neural network, the target features are extracted to achieve automatic detection of dangerous goods. The purpose of better identification. In the simulation experiment, using the public data set and self-built data set in the X-ray security inspection field, the accuracy of the identification of dangerous goods in the X-ray image was obtained more satisfactory results than the traditional dangerous goods identification. The improved Alex Net network’ s testing accuracy on contraband knives and guns is 8.53% and 11.6% higher than the training accuracy of the original Alex Net network.

scholarly journals Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Shraddha Mainali ◽  
Marin E. Darsie ◽  
Keaton S. Smetana
Keyword(s):  
Machine Learning ◽  
Outcome Prediction ◽  
Clinical Decision Making ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Patient Specific ◽  
Learning Technology ◽  
Learning Tools ◽  
Data Set ◽  
Stroke Diagnosis

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.

A Public Repository of Image-Based Computational Models for Patient-Specific Blood Flow Simulation

2012 ◽  
Author(s):  
Nathan M. Wilson ◽  
Ana K. Ortiz ◽  
Allison B. Johnson ◽  
Frank R. Arko ◽  
Jeffrey A. Feinstein ◽  
...  
Keyword(s):  
Blood Flow ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Flow Simulation ◽  
Clinical Decision ◽  
Arterial Disease ◽  
Patient Specific ◽  
Blood Flow Simulation ◽  
Coronary Arterial Disease ◽  
Public Data

Over the past two decades, significant progress has been made on increasing the realism and fidelity of image-based patient-specific blood flow simulation. A clear example of this progress is the first-of-a-kind multi-center clinical trial under way by Heartflow, Inc. (Redwood City, CA) attempting to utilize blood flow simulation in clinical decision making for coronary arterial disease. While recent applications of patient-specific blood flow simulation are impressive, numerous opportunities still exist for its application in advanced research in disease progression, design of better medical devices, and additional clinical applications for patient-specific interventional planning. Three core challenges face researchers in this space. First, state-of-the art techniques for patient-specific anatomic model construction and hemodynamic simulation require specialized, complex software. In recent years, open-source initiatives such as SimVascular and VMTK have addressed this need. Second, the access to clinical data has traditionally been limited to those with strong ties to research hospitals. Finally, public data for verification and validation of computational models for blood flow has also been limited.

Predicting future AI failures from historic examples

foresight ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 138-152 ◽  
Author(s):  
Roman V. Yampolskiy
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Design Methodology ◽  
Security System ◽  
Moderate Level ◽  
Catastrophic Event ◽  
Data Set ◽  
Content Type ◽  
Public Data ◽  
Ai Safety

Purpose The purpose of this paper is to explain to readers how intelligent systems can fail and how artificial intelligence (AI) safety is different from cybersecurity. The goal of cybersecurity is to reduce the number of successful attacks on the system; the goal of AI Safety is to make sure zero attacks succeed in bypassing the safety mechanisms. Unfortunately, such a level of performance is unachievable. Every security system will eventually fail; there is no such thing as a 100 per cent secure system. Design/methodology/approach AI Safety can be improved based on ideas developed by cybersecurity experts. For narrow AI Safety, failures are at the same, moderate level of criticality as in cybersecurity; however, for general AI, failures have a fundamentally different impact. A single failure of a superintelligent system may cause a catastrophic event without a chance for recovery. Findings In this paper, the authors present and analyze reported failures of artificially intelligent systems and extrapolate our analysis to future AIs. The authors suggest that both the frequency and the seriousness of future AI failures will steadily increase. Originality/value This is a first attempt to assemble a public data set of AI failures and is extremely valuable to AI Safety researchers.

scholarly journals Complications following cranioplasty: incidence and predictors in 348 cases

2015 ◽  
Vol 123 (1) ◽  
pp. 182-188 ◽  
Author(s):  
Mario Zanaty ◽  
Nohra Chalouhi ◽  
Robert M. Starke ◽  
Shannon W. Clark ◽  
Cory D. Bovenzi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multivariate Analysis ◽  
Clinical Decision Making ◽  
Early Recognition ◽  
Graft Infection ◽  
Multivariate Logistic Regression Analysis ◽  
Patient Specific ◽  
Graft Material ◽  
Hematoma Evacuation ◽  
Increased Risk

OBJECT The factors that contribute to periprocedural complications following cranioplasty, including patient-specific and surgery-specific factors, need to be thoroughly assessed. The aim of this study was to evaluate risk factors that predispose patients to an increased risk of cranioplasty complications and death. METHODS The authors conducted a retrospective review of all patients at their institution who underwent cranioplasty following craniectomy for stroke, subarachnoid hemorrhage, epidural hematoma, subdural hematoma, and trauma between January 2000 and December 2011. The following predictors were tested: age, sex, race, diabetic status, hypertensive status, tobacco use, reason for craniectomy, urgency status of the craniectomy, graft material, and location of cranioplasty. The cranioplasty complications included reoperation for hematoma, hydrocephalus postcranioplasty, postcranioplasty seizures, and cranioplasty graft infection. A multivariate logistic regression analysis was performed. Confidence intervals were calculated as the 95% CI. RESULTS Three hundred forty-eight patients were included in the study. The overall complication rate was 31.32% (109 of 348). The mortality rate was 3.16%. Predictors of overall complications in multivariate analysis were hypertension (OR 1.92, CI 1.22–3.02), increasing age (OR 1.02, CI 1.00–1.04), and hemorrhagic stroke (OR 3.84, CI 1.93–7.63). Predictors of mortality in multivariate analysis were diabetes mellitus (OR 7.56, CI 1.56–36.58), seizures (OR 7.25, CI 1.238–42.79), bifrontal cranioplasty (OR 5.40, CI 1.20–24.27), and repeated surgery for hematoma evacuation (OR 13.00, CI 1.51–112.02). Multivariate analysis was also applied to identify the variables that affect the development of seizures, the need for reoperation for hematoma evacuation, the development of hydrocephalus, and the development of infections. CONCLUSIONS The authors' goal was to provide the neurosurgeon with predictors of morbidity and mortality that could be incorporated in the clinical decision-making algorithm. Control of a patient's risk factors and early recognition of complications may help practitioners avoid the exhaustive list of complications.

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