Breast cancer risk prediction using machine learning: a systematic review

BackgroundBreast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies.Objective and methodsThis study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers.ResultsA total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI.ConclusionThis study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features

BAYESIAN BASED BRAIN SOURCE LOCALIZATION TECHNIQUE USING EEG SIGNALS

Brain Source localization from EEG/MEG is an ill-posed inverse problem with high uncertainty in the solution. This source localization information is used to diagnose various brain disorders such as epilepsy, schizophrenia, stress, depression and Alzheimer

BRAIN SOURCE LOCALIZATION TECHNIQUE FOR EEG SIGNALS BASED ON ENHANCED MULTIPLE SPARSE PRIORS

The brain source localization information is used to diagnose various brain disorders such as epilepsy, schizophrenia, stress, depression and Alzheimer. It is an ill-posed problem in nature affected by uncertainty in solution. Different algorithms are proposed for the solution of this ill-posed problem which include minimum norm estimation (MNE), second order Laplacian based low resolution brain electromagnetic tomography (LORETA), standardized LORETA (sLORETA), exact LORETA, subspace based multiple signal classifier (MUSIC), Beamformer and Bayesian framework based multiple source priors (MSP). The solution provided by each of the algorithms mentioned above is characterized by various parameters which include the accuracy, computational complexity and localization error. The existing algorithms suffer from low resolution (LORETA family), high computational time (subspace algorithms and FOCUSS, WMN-LORETA) and no validation

Introductory survey for wireless infrared communications

Wireless infrared communications can be defined as the propagation of light waves in free space using infrared radiation whose range is 400–700 nm. This range corresponds to frequencies of hundreds of terahertz, which is high for higher data rate applications. Wireless infrared is applied for higher data rates applications such as wireless computing, wireless video and wireless multimedia communication applications. Introduced by Gfeller, this field has grown with different link configurations, improved transmitter efficiency, increased receiver responsivity and various multiple access techniques for improved quality. Errors are caused because of background light, which causes degradation overall system performance. Error correction techniques are used to remove the errors caused during transmission. This study provides a brief account on field theory used for error correction in wireless infrared systems. The results are produced in terms of bit error rate and signal-to-noise ratio for various bit lengths to show the ability of encoding and decoding algorithms

The competency-based training & assessment, and improvement of technical competencies and changes in pedagogical behavior

This research aims to analyze the improvement of technical competencies and changes in pedagogical behavior on competency-based training and assessment. We developed a modified conceptual model with the help of previous literature. We examined the impact of competency-based training & assessment on traditional teaching methodologies, practical teaching strategies, technical knowledge, and pedagogical & technical components as outcome variables. We also incorporated behavior & psychological aspects of teachers as mediators in our modified conceptual model and examined the mediation between exogenous and endogenous variables. We have collected the data of 458 respondents using a modified structured questionnaire from public and private teachers of Sindh technical and vocational institutions online from different regions of Sindh, Pakistan. For the data analyses, we employed SEM-based multivariate techniques. The findings exhibit that the direct relationship between competency-based training & assessment (independent variable) has a significant and positive influence on traditional teaching methodologies, practical teaching strategies, technical knowledge, and pedagogical & technical components as outcome variables. Similarly, the findings further concluded that behavior & psychological aspects are potent mediators between competency-based training & assessment and traditional teaching methodologies, teaching-learning abilities, practical teaching strategies, technical knowledge, and pedagogical & technical components as outcome variables. Hence, the behavior & psychological aspects of teachers is a vital factor that accelerates the impact of competency-based training & assessment on the dependent variables. The outcomes of this research are imperative theoretical and practical implications, which may guide the direction of future researchers and policymakers of vocational training and education