The Applications of Genetic Algorithms in Medicine

Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review

Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast Machine Learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001-2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications.

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics

Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.

Multidisciplinary considerations of fairness in medical AI: A scoping review

INTRODUCTION

Artificial Intelligence (AI) technology has been developed significantly in recent years. The fairness of medical AI is of great concern due to its direct relation to human life and health. This review aims to analyze the existing research literature on fairness in medical AI from the perspectives of computer science, medical science, and social science (including law and ethics). The objective of the review is to examine the similarities and differences in the understanding of fairness, explore influencing factors, and investigate potential measures to implement fairness in medical AI across English and Chinese literature.

METHODS

This study employed a scoping review methodology and selected the following databases: Web of Science, MEDLINE, Pubmed, OVID, CNKI, WANFANG Data, etc., for the fairness issues in medical AI through February 2023. The search was conducted using various keywords such as "artificial intelligence," "machine learning," "medical," "algorithm," "fairness," "decision-making," and "bias." The collected data were charted, synthesized, and subjected to descriptive and thematic analysis.

RESULTS

After reviewing 468 English papers and 356 Chinese papers, 53 and 42 were included in the final analysis. Our results show the three different disciplines all show significant differences in the research on the core issues. Data is the foundation that affects medical AI fairness in addition to algorithmic bias and human bias. Legal, ethical, and technological measures all promote the implementation of medical AI fairness.

CONCLUSIONS

Our review indicates a consensus regarding the importance of data fairness as the foundation for achieving fairness in medical AI across multidisciplinary perspectives. However, there are substantial discrepancies in core aspects such as the concept, influencing factors, and implementation measures of fairness in medical AI. Consequently, future research should facilitate interdisciplinary discussions to bridge the cognitive gaps between different fields and enhance the practical implementation of fairness in medical AI.

Can in vitro embryo production be estimated from semen variables in Senepol breed by using artificial intelligence?

Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.

Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures

Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.

Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient's health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8-0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.

Distinguishing Healthy and Carcinoma Cell Cultures Using Fluorescence Spectra Decomposition with a Genetic-Algorithm-Based Code

In this paper, we analysed the steady state fluorescence spectra of cell suspensions containing healthy and carcinoma fibroblast mouse cells, using a genetic-algorithm-spectra-decomposition software (GASpeD). In contrast to other deconvolution algorithms, such as polynomial or linear unmixing software, GASpeD takes into account light scatter. In cell suspensions, light scatter plays an important role as it depends on the number of cells, their size, shape, and coagulation. The measured fluorescence spectra were normalized, smoothed and deconvoluted into four peaks and background. The wavelengths of intensities' maxima of lipopigments (LR), FAD, and free/bound NAD(P)H (AF/AB) of the deconvoluted spectra matched published data. In deconvoluted spectra at pH = 7, the fluorescence intensities of the AF/AB ratio in healthy cells was always higher in comparison to carcinoma cells. In addition, the AF/AB ratio in healthy and carcinoma cells were influenced differently by changes in pH. In mixtures of healthy and carcinoma cells, AF/AB decreases when more than 13% of carcinoma cells are present. Expensive instrumentation is not required, and the software is user friendly. Due to these attributes, we hope that this study will be a first step in the development of new cancer biosensors and treatments with the use of optical fibers.

Identify Hard-to-Place Kidneys for Early Engagement in Accelerated Placement With a Deep Learning Optimization Approach

Recommended practices that follow match-run sequences for hard-to-place kidneys succumb to many declines, accruing cold ischemic time and exacerbating kidney quality that may lead to unnecessary kidney discard. Hard-to-place deceased donor kidneys accepted and transplanted later in the match-run sequence may threaten higher graft failure rates. Accelerated placement is a practice for organ procurement organizations (OPOs) to allocate high-risk kidneys out of sequence and reach patients at aggressive transplant centers. The current practice of assessing hard-to-place kidneys and engaging in accelerated kidney placements relies heavily on the kidney donor profile index (KDPI) and the number of declines. Although this practice is reasonable, it also accrues cold ischemic time and increases the risk for kidney discard. We use a deep learning optimization approach to quickly identify kidneys at risk for discard. This approach uses Organ Procurement and Transplantation Network data to model kidney disposition. We filter discards and develop a model to predict transplant and discard of recovered and not transplanted kidneys. Kidneys with a higher probability of discard are deemed hard-to-place kidneys, which require early engagement for accelerated placement. Our approach will aid in identifying hard-to-place kidneys before or after procurement and support OPOs to deviate from the match-run for accelerated placement. Compared with the KDPI-only prediction of the kidney disposition, our approach demonstrates a 10% increase in correctly predicting kidneys at risk for discard. Future work will include developing models to identify candidates with an increased benefit from using hard-to-place kidneys.

Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data

The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer's disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model's efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server.

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

Cell-Level Spatio-Temporal Model for a Bacillus Calmette–Guérin-Based Immunotherapy Treatment Protocol of Superficial Bladder Cancer

Bladder cancer is one of the most widespread types of cancer. Multiple treatments for non-invasive, superficial bladder cancer have been proposed over the last several decades with a weekly Bacillus Calmette–Guérin immunotherapy-based therapy protocol, which is considered the gold standard today. Nonetheless, due to the complexity of the interactions between the immune system, healthy cells, and cancer cells in the bladder’s microenvironment, clinical outcomes vary significantly among patients. Mathematical models are shown to be effective in predicting the treatment outcome based on the patient’s clinical condition at the beginning of the treatment. Even so, these models still have large errors for long-term treatments and patients that they do not fit. In this work, we utilize modern mathematical tools and propose a novel cell-level spatio-temporal mathematical model that takes into consideration the cell–cell and cell–environment interactions occurring in a realistic bladder’s geometric configuration in order to reduce these errors. We implement the model using the agent-based simulation approach, showing the impacts of different cancer tumor sizes and locations at the beginning of the treatment on the clinical outcomes for today’s gold-standard treatment protocol. In addition, we propose a genetic-algorithm-based approach to finding a successful and time-optimal treatment protocol for a given patient’s initial condition. Our results show that the current standard treatment protocol can be modified to produce cancer-free equilibrium for deeper cancer cells in the urothelium if the cancer cells’ spatial distribution is known, resulting in a greater success rate.

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.

Minimally-Invasive and Efficient Method to Accurately Fit the Bergman Minimal Model to Diabetes Type 2

Introduction

Diabetes mellitus is a global burden that is expected to grow 25 % by 2030. This will increase the need for prevention, diagnosis and treatment of diabetes. Animal and individualized in silico models will allow understanding and compensation for inter and intra-individual differences in treatment and management strategies for diabetic patients. The method presented here can advance the concept of personalized medicine.

Methods

Twenty experiments were performed with Sprague-Dawley rats with streptozotocin induced experimental diabetes in which the insulin-glucose response curve was recorded over 60-100 min using only an insulin pump and a percutaneous glucose sensor. The information was used to fit the five-parameter Bergman Minimal Model to the experimental results using a genetic algorithm with a root-mean-squared optimization rule.

Results

The Bergman Minimal Model parameters were estimated with high accuracy, low prediction bias, and low average root-mean-squared error of 15.27 mg/dl glucose.

Conclusions

This study demonstrates a simple method to accurately parameterize the Bergman Minimal Model. We used Sprague-Dawley rats since their physiology is close to that of humans. The parameters can be used to objectively characterize the physiological severity of diabetes. In this way, planned treatments can compensate for natural variations of conditions both inter and intra patients. Changes in parameters indicate the patient's diabetic condition using values of glucose effectiveness ( S G = p 1 ) and insulin sensitivity ( S I = p 3 / p 2 ). Quantifying the diabetic patient's condition is consistent with the trend toward personalized medicine. Parameter values can also be used to explain atypical research results of other studies and increase understanding of diabetes.

Serum Biomarkers and Classification and Regression Trees Can Discriminate Symptomatic from Asymptomatic Carotid Artery Disease Patients

Objective

To assess biomarkers between symptomatic and asymptomatic patients, and to construct a classification and regression tree (CART) algorithm for their discrimination.

Patients and Methods

136 patients were enrolled. They were symptomatic (high risk) (N = 82, stenosis degree ≥ 50%, proven to be responsible for ischemic stroke the last six months) and asymptomatic (low risk) (N = 54, stenosis degree ≤ 50%). Levels of fibrinogen, matrix metalloproteinase-1 (MMP-1), tissue inhibitor of metalloproteinase-1 (TIMP-1), soluble intercellular adhesion molecule (SiCAM), soluble vascular cell adhesion molecule (SvCAM), adiponectin and insulin were measured on a Luminex 3D platform and their differences were evaluated; subsequently, a CART model was created and evaluated.

Results

All measured biomarkers, except adiponectin, had significantly higher levels in symptomatic patients. The constructed CART prognostic model had 97.6% discrimination accuracy on symptomatic patients and 79.6% on asymptomatic, while the overall accuracy was 90.4%. Moreover, the population was split into training and test sets for CART validation.

Conclusion

Significant differences were found in the biomarkers between symptomatic and asymptomatic patients. The CART model proved to be a simple decision-making algorithm linked with risk probabilities and provided evidence to identify and, therefore, treat patients being at high risk for cardiovascular disease.

Diffuse Reflectance Parameters of Treated Leishmaniasis Cutaneous Ulcers and Association with Histopathologies in an Animal Model: A Proof of Concept

Cutaneous leishmaniasis (CL) is a parasitic disease that produces chronic skin ulcers. Although it has a worldwide presence, it is a neglected disease that still requires novel tools for its management. In order to study the use of optical tools in CL, this article presents a preliminary study of the correlation between CL histopathological and optical parameters. Optical parameters correspond to absorption and scattering coefficients obtained from diffuse reflectance spectra of treated CL in golden hamsters. Independently, histopathological data were collected from the same hamsters. As a result, after Spearman correlation and the Kruskal-Wallis test, inverse correlation was found between absorption/scattering optical parameters and inflammatory histopathological values, such as the scattering parameter related to the diameter of fibroblasts with the histopathological parameters of fibrosis, polymorphonuclear neutrophils, lymphocytes, plasmocytes, hyperplasia, and Leishmania, and the absorption parameter oxygen saturation showed a relation with the granulation tissue histopathological parameter. These correlations agree with the expected behavior of tissue composition during the healing process in CL. The results correspond to a proof of concept that shows that optical diffuse reflectance-based tools and methods could be considered as an alternative to assist in CL diagnosis and treatment follow-up.

Application of Design of Experiments® Approach-Driven Artificial Intelligence and Machine Learning for Systematic Optimization of Reverse Phase High Performance Liquid Chromatography Method to Analyze Simultaneously Two Drugs (Cyclosporin A and Etodolac) in Solution, Human Plasma, Nanocapsules, and Emulsions

The objectives of current investigation are (1) to find out wavelength of maximum absorbance (λ_max) for combined cyclosporin A and etodolac solution followed by selection of mobile phase suitable for the RP-HPLC method, (2) to define analytical target profile and critical analytical attributes (CAAs) for the analytical quality by design, (3) to screen critical method parameters with the help of full factorial design followed by optimization with face-centered central composite design (CCD) approach-driven artificial neural network (ANN)-linked with the Levenberg-Marquardt (LM) algorithm for finding the RP-HPLC conditions, (4) to perform validation of analytical procedures (trueness, linearity, precision, robustness, specificity and sensitivity) using combined drug solution, and (5) to determine drug entrapment efficiency value in dual drug-loaded nanocapsules/emulsions, percentage recovery value in human plasma spiked with two drugs and solution state stability analysis at different stress conditions for substantiating the double-stage systematically optimized RP-HPLC method conditions. Through isobestic point and scouting step, 205 nm and ACN:H₂O mixture (74:26) were selected respectively as the λ_max and mobile phase. The ANN topology (3:10:4) indicating the input, hidden and output layers were generated by taking the 20 trials produced from the face-centered CCD model. The ANN-linked LM model produced minimal differences between predicted and observed values of output parameters (or CAAs), low mean squared error and higher correlation coefficient values in comparison to the respective values produced by face-centered CCD model. The optimized RP-HPLC method could be applied to analyze two drugs concurrently in different formulations, human plasma and solution state stability checking.

Artificial intelligence in oncology: From bench to clinic

In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproducibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.

Feasibility Study of Robust Optimization to Reduce Dose Delivery Uncertainty by Potential Applicator Displacements for a Cervix Brachytherapy

Brachytherapy is an important technique to increase the overall survival of cervical cancer patients. However, a possible shift of the applicators in relation to the target and organs at risk may occur between imaging and treatment. Without daily adaptive brachytherapy planning, these applicator displacements can lead to a significant change in dose distribution. In order to resolve it, a robust optimization method had been developed using a genetic algorithm combined with a median absolute deviation as a robustness evaluation function. The resulting robustness plans from our strategy might be worth considering according to the GEC-ESTRO guidelines. From the point of view of dose delivery uncertainty from applicator displacement, the robust optimization may be considered with caution in a single-plan approach for High Dose Rate brachytherapy treatment planning and should be confirmed by a more thorough investigation.

A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis

This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods.

A review on genetic algorithm: past, present, and future

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study

RESEARCH QUESTION

The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology.

DESIGN

This retrospective cohort study included 212 patients who underwent single blastocyst transfer at IVI Valencia. A single image of each of 186 embryos was studied, and the protein profile was analysed in 81 samples of spent embryo culture medium from patients included in the preimplantation genetic testing programme. The information extracted from the analyses was used as input data for the ANN. The multilayer perceptron and the back-propagation learning method were used to train the ANN. Finally, predictive power was measured using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Three ANN architectures classified most of the embryos correctly as leading (LB+) or not leading (LB-) to a live birth: 100.0% for ANN1 (morphological variables and two proteins), 85.7% for ANN2 (morphological variables and seven proteins), and 83.3% for ANN3 (morphological variables and 25 proteins). The artificial intelligence model using information extracted from blastocyst image analysis and concentrations of interleukin-6 and matrix metalloproteinase-1 was able to predict live birth with an AUC of 1.0.

CONCLUSIONS

The model proposed in this preliminary report may provide a promising tool to select the embryo most likely to lead to a live birth in a euploid cohort. The accuracy of prediction demonstrated by this software may improve the efficacy of an assisted reproduction treatment by reducing the number of transfers per patient. Prospective studies are, however, needed.

Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI

Accurate detection of neuro-psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) using resting state functional Magnetic Resonance Imaging (rs-fMRI) is challenging due to high dimensionality of input features, low inter-class separability, small sample size and high intra-class variability. For automatic diagnosis of ADHD and autism, spatial transformation methods have gained significance and have achieved improved classification performance. However, they are not reliable due to lack of generalization in dataset like ADHD with high variance and small sample size. Therefore, in this paper, we present a Metaheuristic Spatial Transformation (MST) approach to convert the spatial filter design problem into a constraint optimization problem, and obtain the solution using a hybrid genetic algorithm. Highly separable features obtained from the MST along with meta-cognitive radial basis function based classifier are utilized to accurately classify ADHD. The performance was evaluated using the ADHD200 consortium dataset using a ten fold cross validation. The results indicate that the MST based classifier produces state of the art classification accuracy of 72.10% (1.71% improvement over previous transformation based methods). Moreover, using MST based classifier the training and testing specificity increased significantly over previous methods in literature. These results clearly indicate that MST enables the determination of the highly discriminant transformation in dataset with high variability, small sample size and large number of features. Further, the performance on the ADHD200 dataset shows that MST based classifier can be reliably used for the accurate diagnosis of ADHD using rs-fMRI.Clinical relevance- Metaheuristic Spatial Transformation (MST) enables reliable and accurate detection of neuropsychological disorders like ADHD from rs-fMRI data characterized by high variability, small sample size and large number of features.

Speed Control Optimization for Autonomous Vehicles with Metaheuristics

The development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional – Integral – Derivative (PID) controllers are the most widely used in order to meet the requirements of cruise control. However, fine tuning of the parameters associated with this type of controller can be complex, especially if it is intended to optimize them and reduce their characteristic errors. The objective of the work described in this paper is to evaluate the capacity of several metaheuristics for the adjustment of the parameters Kp, 1/Ti, and 1/Td of a PID controller to regulate the speed of a vehicle. To do this, an adjustment error function has been established from a linear combination of classic estimators of the goodness of the controller, such as overshoot, settling time (ts), steady-state error (ess), and the number of changes of sign of the signal (d). The error obtained when applying the controller has also been compared to a computational model of the vehicle after estimating the parameters Kp, Ki, and Kd, both for a setpoint sequence used in the adjustment of the system parameters and for a sequence not used during the adjustment, and therefore unknown by the system. The main novelty of the paper is to propose a new global error function, a function that enables the use of heuristic optimization methods for PID tuning. This optimization has been carried out by using three methods: genetic algorithms (GA), memetics algorithms (MA), and mesh adaptive direct search (MADS). The results of the application of the optimization methods using the proposed metric show that the accuracy of the PID controller is improved, compared with the classical optimization based on classical methods like the integral absolute error (IAE) or similar metrics, reducing oscillatory behaviours as well as minimizing the analysed performance indexes.

Metaheuristics Optimizations for Speed Regulation in Self Driving Vehicles

The speed regulation becomes an important necessity in the self –driving vehicles that are engaged in various driving chores. It prevails as a prominent area of research from the past decades, proportional, integral and the derivative controllers play significant role in regulating the movement velocity of the vehicles as perfect adjustments of the parameters linked with the controller could afford to provide a proper speed regulation. But the attaining a perfect adjustments in the parameters are highly tedious. To attain a proper speed regulation in the self-driving vehicles, the paper attempts to utilize the metaheuristics algorithms for optimizing the parameters and minimizing the errors associated with its attributes. A regulating function to fine tune the proportional derivative and the integral controller parameters is formulated in the proffered method and the proper adjustment is achieved utilizing the heuristic optimization. Triple algorithms, genetic (Ge-Al), memetics (Me-Al) and adaptive direct search based on mesh (M-ADS) is used in the proffered method to carry out the optimizations. The results on applying the proposed optimization techniques proves to be more accurate compared to the conventional optimization techniques that were employed in adjusting the absolute error that is integral and the minimizing oscillatory performances and the performance index.

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

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.

Shaping of a laser-accelerated proton beam for radiobiology applications via genetic algorithm

Laser-accelerated protons have a great potential for innovative experiments in radiation biology due to the sub-picosecond pulse duration and high dose rate achievable. However, the broad angular divergence makes them not optimal for applications with stringent requirements on dose homogeneity and total flux at the irradiated target. The strategy otherwise adopted to increase the homogeneity is to increase the distance between the source and the irradiation plane or to spread the beam with flat scattering systems or through the transport system itself. Such methods considerably reduce the proton flux and are not optimal for laser-accelerated protons. In this paper we demonstrate the use of a Genetic Algorithm (GA) to design an optimal non-flat scattering system to shape the beam and efficiently flatten the transversal dose distribution at the irradiated target. The system is placed in the magnetic transport system to take advantage of the presence of chromatic focusing elements to further mix the proton trajectories. The effect of a flat scattering system placed after the transport system is also presented for comparison. The general structure of the GA and its application to the shaping of a laser-accelerated proton beam are presented, as well as its application to the optimisation of dose distribution in a water target in air.

Prediction of Effective Lens Position Using Multiobjective Evolutionary Algorithm

Purpose

The purpose of this study was to evaluate the prediction accuracy of effective lens position (ELP) after cataract surgery using a multiobjective evolutionary algorithm (MOEA).

Methods

Ninety-six eyes of 96 consecutive patients (aged 73.9 ± 8.6 years) who underwent cataract surgery were retrospectively studied; the eyes were randomly distributed to a prediction group (55 eyes) and a verification group (41 eyes). The procedure was repeated randomly 30 times to create 30 data sets for both groups. In the prediction group, based on the parameters of preoperative optical coherence tomography (OCT), biometry, and anterior segment (AS)-OCT, the prediction equation of ELP was created using MOEA and stepwise multiple regression analysis (SMR). Subsequently, the prediction accuracy of ELPs was evaluated and compared with conventional formulas, including SRK/T and the Haigis formula.

Results

The rate of mean absolute prediction error of 0.3 mm or higher was significantly lower in MOEA (mean 4.9% ± 3.2%, maximum 9.8%) than SMR (mean 7.3% ± 4.8%, maximum 24.4%) (P = 0.0323). The median of the correlation coefficient (R² = 0.771) between the MOEA predicted and measured ELP was higher than the SRK/T (R² = 0.412) and Haigis (R² = 0.438) formulas.

Conclusions

The study demonstrated that ELP prediction by MOEA was more accurate and was a method of less fluctuation than that of SMR and conventional formulas.

Translational Relevance

MOEA is a promising method for solving clinical problems such as prediction of ocular biometry values by simultaneously optimizing several conditions for subjects affected by various complex factors.

Development of MART for the Rapid Production of Nanostructured Lipid Carriers Loaded with All-Trans Retinoic Acid for Dermal Delivery

All-trans retinoic acid (ATRA) has been regarded as a wonder drug for many dermatological complications; however, its application is limited due to the extreme irritation, and toxicity seen once it has sufficiently concentrated into the bloodstream from the skin. Thus, the present study was aimed to increase the entrapment of ATRA and minimize its transdermal permeation. ATRA incorporated within nanostructured lipid carriers (NLCs) were produced by a green and facile thin lipid-film based microwave-assisted rapid technique (MART). The optimization was carried out using the response surface methodology (RSM)-driven artificial neural network (ANN) coupled with genetic algorithm (GA). The liquid lipid and surfactants were seen to play a very crucial role culminating in the particle size (< 70 nm), zeta potential (< - 32 mV), and entrapment of ATRA (> 98%). ANN-GA-optimized NLCs required a minimal quantity of the surfactants, formed within 2 min and were stable for 1 year at different storage conditions. The optimized NLC-loaded creams showed a skin retention (ex vivo) to an extent of 87.42% with no detectable drug in the receptor fluid (24 h) in comparison to the marketed cream which released 47.32% (12 h) of ATRA. The results were in good correlation with the in vivo skin deposition studies. The NLCs were biocompatible and non-skin irritant based on the primary irritation index. In conclusion, the NLCs were seen to have a very high potential in overcoming the drawbacks of ATRA for dermal delivery and could be produced conveniently by the MART.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

Warfarin maintenance dose Prediction for Patients undergoing heart valve replacement- a hybrid model with genetic algorithm and Back-Propagation neural network

Warfarin is the most recommended anticoagulant drug for patients undergoing heart valve replacement. However, due to the narrow therapeutic window and individual dose, the use of warfarin needs more advanced technology. We used the data collected from a multi-central registered clinical system all over China about the patients who have undergone heart valve replacement, subsequently divided into three groups (training group: 10673 cases; internal validation group: 3558 cases; external validation group: 1463 cases) in order to construct a hybrid model with genetic algorithm and Back-Propagation neural network (BP-GA), For testing the model's prediction accuracy, we used Mean absolute error (MAE), Root mean squared error (RMSE) and the ideal predicted percentage of total and dose subgroups. In results, whether in internal or in external validation group, the total ideal predicted percentage was over 58% while the intermediate dose subgroup manifested the best. Moreover, it showed higher prediction accuracy, lower MAE value and lower RMSE value in the external validation group than that in the internal validation group (p < 0.05). In conclusion, BP-GA model is promising to predict warfarin maintenance dose.

Preliminary Application of WCX Magnetic Bead-Based Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry in Analyzing the Urine of Renal Clear Cell Carcinoma

Objective To evaluate the application of weak cation exchange (WCX) magnetic bead-based Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) in detecting differentially expressed proteins in the urine of renal clear cell carcinoma (RCCC) and its value in the early diagnosis of RCCC.Methods Eleven newly diagnosed patients (10 males and 1 female, aged 46-78, mean 63 years) of renal clear cell carcinoma by biopsy and 10 healthy volunteers (all males, aged 25-32, mean 29.7 years) were enrolled in this study. Urine samples of the RCCC patients and healthy controls were collected in the morning. Weak cation exchange (WCX) bead-based MALDI-TOF MS technique was applied in detecting differential protein peaks in the urine of RCCC. ClinProTools2.2 software was utilized to determine the characteristic proteins in the urine of RCCC patients for the predictive model of RCCC. Results The technique identified 160 protein peaks in the urine that were different between RCCC patients and health controls; and among them, there was one peak (molecular weight of 2221.71 Da) with statistical significance (P=0.0304). With genetic algorithms and the support vector machine, we screened out 13 characteristic protein peaks for the predictive model. Conclusions The application of WCX magnetic bead-based MALDI-TOF MS in detecting differentially expressed proteins in urine may have potential value for the early diagnosis of RCCC.

DisTeam: A decision support tool for surgical team selection

OBJECTIVE

Surgical service providers play a crucial role in the healthcare system. Amongst all the influencing factors, surgical team selection might affect the patients' outcome significantly. The performance of a surgical team not only can depend on the individual members, but it can also depend on the synergy among team members, and could possibly influence patient outcome such as surgical complications. In this paper, we propose a tool for facilitating decision making in surgical team selection based on considering history of the surgical team, as well as the specific characteristics of each patient.

METHODS

DisTeam (a decision support tool for surgical team selection) is a metaheuristic framework for objective evaluation of surgical teams and finding the optimal team for a given patient, in terms of number of complications. It identifies a ranked list of surgical teams personalized for each patient, based on prior performance of the surgical teams. DisTeam takes into account the surgical complications associated with teams and their members, their teamwork history, as well as patient's specific characteristics such as age, body mass index (BMI) and Charlson comorbidity index score.

RESULTS

We tested DisTeam using intra-operative data from 6065 unique orthopedic surgery cases. Our results suggest high effectiveness of the proposed system in a health-care setting. The proposed framework converges quickly to the optimal solution and provides two sets of answers: a) The best surgical team over all the generations, and b) The best population which consists of different teams that can be used as an alternative solution. This increases the flexibility of the system as a complementary decision support tool.

CONCLUSION

DisTeam is a decision support tool for assisting in surgical team selection. It can facilitate the job of scheduling personnel in the hospital which involves an overwhelming number of factors pertaining to patients, individual team members, and team dynamics and can be used to compose patient-personalized surgical teams with minimum (potential) surgical complications.