X-Ray Image Classification Methods using Artificial Intelligence Techniques

This paper aims at providing a possible solution to the problem posed by the lack of automation in classifying X-Ray images taken in hospitals. Daily, all over the world, a tremendous number of digital X-Ray images are taken. Currently, there is a need for specialized medical staff to classify various such images. This process takes time, and some situations need immediate intervention, making it crucial that this procedure takes as little as possible. Besides the need for a prompt intervention, the accuracy of the diagnosis is paramount. Several hospital X-Ray datasets pose a major problem, consisting of inaccurately populated datasets. This happens when a patient has multiple scans scheduled in the same day, each focusing on a different body area. In numerous cases, they are classified as a single entry, resulting in datasets being populated with images of wrong body parts or containing several empty files. A tremendous amount of time and effort would be needed to get the datasets to a usable state for high-level studies, with specialized staff having to reorganize them manually. For this exact reason, the introduction of such a classification algorithm could possibly create a major breakthrough in the domain of medical imaging with the use of artificial intelligence and machine learning in the medical field

Artificial Intelligent Model for Parkinson’s Disease Management

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disease characterized by both motor and non-motor features. The disease has a significant clinical impact on patients, families, and caregivers through its progressive degenerative effects on mobility and muscle control. Therefore, it is important to discuss the early diagnosis of PD to control the disease of PD patients and prolong their life. The primary objective of our research work was the early diagnosis of patients using Artificial Intelligence (AI). To accomplish that, we developed an Artificial Intelligence Model (model) that diagnoses PD using data collected from patient’s walks. This model is represented by a CNN (Convolutional Neural Network) that finds patterns of Parkinson’s disease in the collected data. The data is represented by multiple images (correlation plots) that are generated from raw data collected using a physiograph created by our team. For the model training, we used data from twelve patients and three healthy people that proved the accuracy of our model. To achieve better results from our AI model, we decided to augment our data by using a Generative Adversarial Network (GAN) that we modeled to generate images containing biomechanical information that represents healthy and patient patterns of Parkinson’s disease. After many hours of training and a series of changes applied to the model architecture, the GAN model demonstrated remarkable advancements in its ability to generate high-quality images that are very similar to the real data collected by us from our study group and generates real patterns of Parkinson’s disease represented in the images. The faithful replication of the correlations matrices by our GAN model helps our classification model to learn the Parkinson’s disease symptoms and patterns represented in the correlation matrices we generated. In the future, we want to increase the complexity of the GAN model and train it to generate specific patterns of Parkinson’s disease based on different inputs, to have a more complex generated dataset that will help us to develop future models

The Future of Critical Care: Innovations in Patient-Centered Technology

In the landscape of modern healthcare, the evolution of critical care has been marked by the integration of innovative technologies and the emergence of patient-centered approaches. This study aimed to explore the potential of Artificial Intelligence (AI) in shaping the future of critical care, using data collected from Centricity High Acuity data warehouse from the Anesthesia and Intensive Care Clinic and the operating theater from Emergency County Clinical Hospital "Pius Brînzeu" Timişoara. The existing healthcare landscape is characterized by the complex balance between technological advances and patient-centered care. The advent of AI presents an opportunity to revolutionize critical care, offering real-time insights and personalized interventions. This research seeks to harness the capabilities of AI to enhance patient outcomes in critical care scenarios. The study was conducted at a tertiary care hospital, using a mixed-methods approach that involved retrospective analysis of patient data from Centricity. The AI algorithms were trained on historical data to predict patient deterioration patterns, enabling timely interventions and proactive management. Results demonstrated that the integration of AI-driven insights from Centricity High Acuity data warehouse significantly improves patient outcomes. AI-assisted interventions led to reduced instances of adverse events, shorter lengths of stay, and improved resource utilization. The AI algorithms demonstrated high accuracy in predicting patient deterioration, enabling early interventions and preventing complications. In conclusion, the integration of AI technology using data from Centricity High Acuity data warehouse holds immense promise for the future of patient-centered critical care. The results indicate that AI-driven interventions can enhance patient outcomes, reduce healthcare costs, and improve resource utilization. As healthcare continues to embrace AI, the potential for transformative advancements in critical care is evident, paving the way for a new era of innovative and personalized patient-centered care