Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review

In recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra- and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry

Engineering News, Fall 2019

https://scholarcommons.scu.edu/eng_news/1043/thumbnail.jp

Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning

Mención Internacional en el título de doctorTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature. This fact facilitates the disease fast-spreading, which, according to the World Health Organization (WHO), in 2021 caused 1.2 million deaths and 9.9 million new cases. Traditionally, TB has been considered a binary disease (latent/active) due to the limited specificity of the traditional diagnostic tests. Such a simple model causes difficulties in the longitudinal assessment of pulmonary affectation needed for the development of novel drugs and to control the spread of the disease. Fortunately, X-Ray Computed Tomography (CT) images enable capturing specific manifestations of TB that are undetectable using regular diagnostic tests, which suffer from limited specificity. In conventional workflows, expert radiologists inspect the CT images. However, this procedure is unfeasible to process the thousands of volume images belonging to the different TB animal models and humans required for a suitable (pre-)clinical trial. To achieve suitable results, automatization of different image analysis processes is a must to quantify TB. It is also advisable to measure the uncertainty associated with this process and model causal relationships between the specific mechanisms that characterize each animal model and its level of damage. Thus, in this thesis, we introduce a set of novel methods based on the state of the art Artificial Intelligence (AI) and Computer Vision (CV). Initially, we present an algorithm to assess Pathological Lung Segmentation (PLS) employing an unsupervised rule-based model which was traditionally considered a needed step before biomarker extraction. This procedure allows robust segmentation in a Mtb. infection model (Dice Similarity Coefficient, DSC, 94%±4%, Hausdorff Distance, HD, 8.64mm±7.36mm) of damaged lungs with lesions attached to the parenchyma and affected by respiratory movement artefacts. Next, a Gaussian Mixture Model ruled by an Expectation-Maximization (EM) algorithm is employed to automatically quantify the burden of Mtb.using biomarkers extracted from the segmented CT images. This approach achieves a strong correlation (R2 ≈ 0.8) between our automatic method and manual extraction. Consequently, Chapter 3 introduces a model to automate the identification of TB lesions and the characterization of disease progression. To this aim, the method employs the Statistical Region Merging algorithm to detect lesions subsequently characterized by texture features that feed a Random Forest (RF) estimator. The proposed procedure enables a selection of a simple but powerful model able to classify abnormal tissue. The latest works base their methodology on Deep Learning (DL). Chapter 4 extends the classification of TB lesions. Namely, we introduce a computational model to infer TB manifestations present in each lung lobe of CT scans by employing the associated radiologist reports as ground truth. We do so instead of using the classical manually delimited segmentation masks. The model adjusts the three-dimensional architecture, V-Net, to a multitask classification context in which loss function is weighted by homoscedastic uncertainty. Besides, the method employs Self-Normalizing Neural Networks (SNNs) for regularization. Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules and F1-scores above 0.85 for the most prevalent TB lesions (i.e., conglomerations, cavitations, consolidations, trees in bud) when considering the whole lung. In Chapter 5, we present a DL model capable of extracting disentangled information from images of different animal models, as well as information of the mechanisms that generate the CT volumes. The method provides the segmentation mask of axial slices from three animal models of different species employing a single trained architecture. It also infers the level of TB damage and generates counterfactual images. So, with this methodology, we offer an alternative to promote generalization and explainable AI models. To sum up, the thesis presents a collection of valuable tools to automate the quantification of pathological lungs and moreover extend the methodology to provide more explainable results which are vital for drug development purposes. Chapter 6 elaborates on these conclusions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: María Jesús Ledesma Carbayo.- Secretario: David Expósito Singh.- Vocal: Clarisa Sánchez Gutiérre

Invariant Scattering Transform for Medical Imaging

Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imaging applications such as segmentation, classification, and registration, which can be integrated into machine learning algorithms for disease detection, diagnosis, and treatment planning. Additionally, combining IST with deep learning approaches has the potential to leverage their strengths and enhance medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms : Principles and Perspectives

Funding Information: This work was supported in part by the National Research Foundation of Korea grant funded by the Korean Government, Ministry of Science and ICT, under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program.Peer reviewe

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

Design Simulation and Assessment of Computer Based Cancer Diagnosis Accuracy using ART 1.0 Algorithm

Today Cancer is spreading heavily and become the most dangerous disease in the world. This disease causes death if not diagnoses before the major stage. Small changes or illness in the human body may transform to the cancer in the body. The main thing in this disease is it is not easily detected in its earlier stage. So this causes the aim to design a computer operated system that can make distinguish between benign (non-cancerous) and malignant (cancerous) mammogram. The proposed system helps doctors to increase the diagnosis accuracy. The above propose system shall be simulated by MATLAB. The ART 1.0 algorithm shall be studied and modified to improve the accuracy of existing ART 1.0 system. The simulation shall be done by obtaining cancer data set from UCI repository. The reason behind choosing ART algorithm because of its characteristic to work on three phases i.e., Recognition, Comparison, Search Phase.  The winning neuron is obtained by finding the dot product of input and weight vector. The neuron having largest dot product be the winner.&nbsp