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Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model

Ferdowsi M, Hasan MM, Habib W

Comput Methods Programs Biomed, 2024 Sep;254:108289.
PMID: 38905988 DOI: 10.1016/j.cmpb.2024.108289

BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In healthcare, artificial intelligence (AI) holds promise for advancing disease risk assessment and treatment outcome prediction. However, machine learning (ML) evolution raises concerns about data privacy and biases, especially in sensitive healthcare applications. The objective is to develop and implement a responsible AI model for CD prediction that prioritize patient privacy, security, ensuring transparency, explainability, fairness, and ethical adherence in healthcare applications.
METHODS: To predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions.
RESULTS: Our investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development.
CONCLUSIONS: Our study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.

Matched MeSH terms: Artificial Intelligence*
Measuring Vertical Jump Height With Artificial Intelligence Through a Cell Phone: A Validity and Reliability Report

Tan ECH, Weng Onn S, Montalvo S

J Strength Cond Res, 2024 Sep 01;38(9):e529-e533.
PMID: 38953840 DOI: 10.1519/JSC.0000000000004854

Erik, HT, Onn, SW, and Montalvo, S. Vertical jump height with artificial intelligence through a cell phone: a validity and reliability report. J Strength Cond Res 38(9): e529-e533, 2024-This study estimated the reliability and validity of an artificial intelligence (AI)-driven model in the My Jump 2 (My Jump Lab ) for estimating vertical jump height compared with the Force Platform (FP). The cross-sectional study involved 88 athletes (33 female and 55 male athletes), performing a total of 264 countermovement jumps with hands on hips. "Jump heights were simultaneously measured using the FP and the My Jump 2 app." The FP estimated jump heights using the impulse-momentum method, whereas My Jump 2 used the flight-time method, with the latter using an AI feature for automated detection of jump take-off and landing. Results indicated high reliability for the AI model (intraclass correlation coefficient [ICC 1,3 ] = 0.980, coefficient of variation [CV] = 4.12) and FP (ICC 1,3 = 0.990, CV = 2.92). Validity assessment showed strong agreement between the AI model and FP (ICC 2,k = 0.973). This was also supported by the Bland-Altman analysis, and the ordinary least products regression revealed no significant systematic or proportional bias. The AI-driven model in My Jump 2 is highly reliable and valid for estimating jump height. Strength and conditioning professionals may use the AI-based mobile app for accurate jump height measurements, offering a practical and efficient alternative to traditional methods.

Matched MeSH terms: Artificial Intelligence*
Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study

Rasmussen ME, Akbarov K, Titovich E, Nijkamp JA, Van Elmpt W, Primdahl H, et al.

JCO Glob Oncol, 2024 Aug;10:e2400173.
PMID: 39236283 DOI: 10.1200/GO.24.00173

PURPOSE: Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs).
MATERIALS AND METHODS: Ninety-seven ROs were randomly assigned to either manual or AI-assisted contouring of eight OARs for two head-and-neck cancer cases with an in-between teaching session on contouring guidelines. Thereby, the effect of teaching (yes/no) and AI-assisted contouring (yes/no) was quantified. Second, ROs completed short-term and long-term follow-up cases all using AI assistance. Contour quality was quantified with Dice Similarity Coefficient (DSC) between ROs' contours and expert consensus contours. Groups were compared using absolute differences in medians with 95% CIs.
RESULTS: AI-assisted contouring without previous teaching increased absolute DSC for optic nerve (by 0.05 [0.01; 0.10]), oral cavity (0.10 [0.06; 0.13]), parotid (0.07 [0.05; 0.12]), spinal cord (0.04 [0.01; 0.06]), and mandible (0.02 [0.01; 0.03]). Contouring time decreased for brain stem (-1.41 [-2.44; -0.25]), mandible (-6.60 [-8.09; -3.35]), optic nerve (-0.19 [-0.47; -0.02]), parotid (-1.80 [-2.66; -0.32]), and thyroid (-1.03 [-2.18; -0.05]). Without AI-assisted contouring, teaching increased DSC for oral cavity (0.05 [0.01; 0.09]) and thyroid (0.04 [0.02; 0.07]), and contouring time increased for mandible (2.36 [-0.51; 5.14]), oral cavity (1.42 [-0.08; 4.14]), and thyroid (1.60 [-0.04; 2.22]).
CONCLUSION: The study suggested that AI-assisted contouring is safe and beneficial to ROs working in LMICs. Prospective clinical trials on AI-assisted contouring should, however, be conducted upon clinical implementation to confirm the effects.

Matched MeSH terms: Artificial Intelligence*
Fulltext Development of vision based multiview gait recognition system with MMUGait database

Ng H, Tan WH, Abdullah J, Tong HL

ScientificWorldJournal, 2014;2014:376569.
PMID: 25143972 DOI: 10.1155/2014/376569

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

Matched MeSH terms: Artificial Intelligence
EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms

Ahirwal MK, Kumar A, Singh GK

IEEE/ACM Trans Comput Biol Bioinform, 2013 Nov-Dec;10(6):1491-504.
PMID: 24407307 DOI: 10.1109/TCBB.2013.119

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

Matched MeSH terms: Artificial Intelligence
Adaptive evolutionary artificial neural networks for pattern classification

Oong TH, Isa NA

IEEE Trans Neural Netw, 2011 Nov;22(11):1823-36.
PMID: 21968733 DOI: 10.1109/TNN.2011.2169426

This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms.

Matched MeSH terms: Artificial Intelligence
A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function

Yap KS, Lim CP, Abidin IZ

IEEE Trans Neural Netw, 2008 Sep;19(9):1641-6.
PMID: 18779094 DOI: 10.1109/TNN.2008.2000992

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.

Matched MeSH terms: Artificial Intelligence
Fulltext A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique

Maktabdar Oghaz M, Maarof MA, Zainal A, Rohani MF, Yaghoubyan SH

PLoS One, 2015;10(8):e0134828.
PMID: 26267377 DOI: 10.1371/journal.pone.0134828

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.

Matched MeSH terms: Artificial Intelligence
Theoretically-derived molecular descriptors important in human intestinal absorption

Agatonovic-Kustrin S, Beresford R, Yusof AP

J Pharm Biomed Anal, 2001 May;25(2):227-37.
PMID: 11275432

A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.

Matched MeSH terms: Artificial Intelligence
Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis

Lim CP, Quek SS, Peh KK

J Pharm Biomed Anal, 2003 Feb 05;31(1):159-68.
PMID: 12560060

This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.

Matched MeSH terms: Artificial Intelligence
Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives

Tham SY, Agatonovic-Kustrin S

J Pharm Biomed Anal, 2002 May 15;28(3-4):581-90.
PMID: 12008137

Quantitative structure-retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selected descriptors with the experimentally derived retention times. Retention time values were used as the network's output and calculated molecular descriptors and mobile phase composition as the inputs. The best model with five input descriptors was chosen, and the significance of the selected descriptors for amino acid separation was examined. Results confirmed the dominant role of the organic modifier in such chromatographic systems in addition to lipophilicity (log P) and molecular size and shape (topological indices) of investigated solutes.

Matched MeSH terms: Artificial Intelligence
Fulltext MYNursingHome: A fully-labelled image dataset for indoor object classification

Ismail A, Ahmad SA, Che Soh A, Hassan MK, Harith HH

Data Brief, 2020 Oct;32:106268.
PMID: 32984464 DOI: 10.1016/j.dib.2020.106268

A fully labelled image dataset serves as a valuable tool for reproducible research inquiries and data processing in various computational areas, such as machine learning, computer vision, artificial intelligence and deep learning. Today's research on ageing is intended to increase awareness on research results and their applications to assist public and private sectors in selecting the right equipments for the elderlies. Many researches related to development of support devices and care equipment had been done to improve the elderly's quality of life. Indoor object detection and classification for autonomous systems require large annotated indoor images for training and testing of smart computer vision applications. This dataset entitled MYNursingHome is an image dataset for commonly used objects surrounding the elderlies in their home cares. Researchers may use this data to build up a recognition aid for the elderlies. This dataset was collected from several nursing homes in Malaysia comprises 37,500 digital images from 25 different indoor object categories including basket bin, bed, bench, cabinet and others.

Matched MeSH terms: Artificial Intelligence
Fulltext The Modern and Digital Transformation of Oral Health Care: A Mini Review

Alauddin MS, Baharuddin AS, Mohd Ghazali MI

Healthcare (Basel), 2021 Jan 25;9(2).
PMID: 33503807 DOI: 10.3390/healthcare9020118

Dentistry is a part of the field of medicine which is advocated in this digital revolution. The increasing trend in dentistry digitalization has led to the advancement in computer-derived data processing and manufacturing. This progress has been exponentially supported by the Internet of medical things (IoMT), big data and analytical algorithm, internet and communication technologies (ICT) including digital social media, augmented and virtual reality (AR and VR), and artificial intelligence (AI). The interplay between these sophisticated digital aspects has dramatically changed the healthcare and biomedical sectors, especially for dentistry. This myriad of applications of technologies will not only be able to streamline oral health care, facilitate workflow, increase oral health at a fraction of the current conventional cost, relieve dentist and dental auxiliary staff from routine and laborious tasks, but also ignite participatory in personalized oral health care. This narrative article review highlights recent dentistry digitalization encompassing technological advancement, limitations, challenges, and conceptual theoretical modern approaches in oral health prevention and care, particularly in ensuring the quality, efficiency, and strategic dental care in the modern era of dentistry.

Matched MeSH terms: Artificial Intelligence
Fulltext Recombinant bromelain production in Escherichia coli: process optimization in shake flask culture by response surface methodology

Muntari B, Amid A, Mel M, Jami MS, Salleh HM

AMB Express, 2012;2:12.
PMID: 22336426 DOI: 10.1186/2191-0855-2-12

Bromelain, a cysteine protease with various therapeutic and industrial applications, was expressed in Escherichia coli, BL21-AI clone, under different cultivation conditions (post-induction temperature, L-arabinose concentration and post-induction period). The optimized conditions by response surface methodology using face centered central composite design were 0.2% (w/v) L-arabinose, 8 hr and 25°C. The analysis of variance coupled with larger value of R2 (0.989) showed that the quadratic model used for the prediction was highly significant (p < 0.05). Under the optimized conditions, the model produced bromelain activity of 9.2 U/mg while validation experiments gave bromelain activity of 9.6 ± 0.02 U/mg at 0.15% (w/v) L-arabinose, 8 hr and 27°C. This study had innovatively developed cultivation conditions for better production of recombinant bromelain in shake flask culture.

Matched MeSH terms: Artificial Intelligence
Need for developing a security robot-based risk management for emerging practices in the workplace using the Advanced Human-Robot Collaboration Model

Zheyuan C, Rahman MA, Tao H, Liu Y, Pengxuan D, Yaseen ZM

Work, 2021;68(3):825-834.
PMID: 33612525 DOI: 10.3233/WOR-203416

BACKGROUND: The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During operations such as repairs, unmanageable, adjustment, and set-up, robots can cause serious and fatal injuries to workers. Collaborative robotics recently plays a rising role in the manufacturing filed, warehouses, mining agriculture, and much more in modern industrial environments. This development advances with many benefits, like higher efficiency, increased productivity, and new challenges like new hazards and risks from the elimination of human and robotic barriers.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.

Matched MeSH terms: Artificial Intelligence
Biosensing human blood clotting factor by dual probes: Evaluation by deep long short-term memory networks in time series forecasting

Gopinath SCB, Ismail ZH, Shapiai MI, Sobran NMM

Biotechnol Appl Biochem, 2021 Apr 09.
PMID: 33835514 DOI: 10.1002/bab.2164

Artificial intelligence of things (AIoT) has become a potential tool for use in a wide range of fields, and its use is expanding in interdisciplinary sciences. On the other hand, in a clinical scenario, human blood-clotting disease (Royal disease) detection has been considered an urgent issue that has to be solved. This study uses AIoT with deep long short-term memory networks for biosensing application and analyzes the potent clinical target, human blood clotting factor IX, by its aptamer/antibody as the probe on the microscaled fingers and gaps of the interdigitated electrode. The earlier results by the current-volt measurements have shown the changes in the surface modification. The limit of detection (LOD) was noticed as 1 pM with the antibody as the probe, whereas the aptamer behaved better with the LOD at 100 fM. The time-series predictions from the AIoT application supported the obtained results with the laboratory analyses using both probes. This application clearly supports the results obtained from the interdigitated electrode sensor as aptamer to be the better option for analyzing the blood clotting defects. The current study supports a great implementation of AIoT in sensing application and can be followed for other clinical biomarkers.

Matched MeSH terms: Artificial Intelligence
Fulltext Usable Mobile App for Community Education on Colorectal Cancer: Development Process and Usability Study

Mohamad Marzuki MF, Yaacob NA, Bin Yaacob NM, Abu Hassan MR, Ahmad SB

JMIR Hum Factors, 2019 Apr 16;6(2):e12103.
PMID: 30990454 DOI: 10.2196/12103

BACKGROUND: Participation in colorectal cancer screening is still low among Malaysians despite the increasing trend of incidence, with more than half of the new cases being detected in the advanced stages. Knowledge improvement might increase screening participation and thus improve the chances of disease detection. With the advancement of communication technology, people nowadays prefer to read from their mobile phone using a Web browser or mobile apps compared with the traditional printed material. Therefore, health education and promotion should adapt this behavior change in educating the community.
OBJECTIVE: This study aimed to document the process of designing and developing a mobile app for community education on colorectal cancer and assess the usability of the prototype.
METHODS: The nominal group technique (NGT) was used for the content development of the mobile app. NGT involving community educationists and clinicians combined with community representatives as the target users identified relevant health information and communication strategies including features for a user-friendly mobile app. The prototype was developed using framework Ionic 1, based on the Apache Cordova and Angular JS (Google). It was published in the Google Play store. In total, 50 mobile phone users aged 50 years and above and who had never been diagnosed with any type of cancer were invited to download and use the app. They were asked to assess the usability of the app using the validated Malay version of System Usability Scale Questionnaire for the Assessment of Mobile Apps questionnaire. The One-sample t test was used to assess the usability score with a cut-off value of 68 for the usable mobile app.
RESULTS: The Colorectal Cancer Awareness Application (ColorApp) was successfully developed in the local Malay language. The NGT discussion had suggested 6 main menus in the ColorApp prototype, which are Introduction, Sign and Symptoms, Risk Factors, Preventive Measures, Colorectal Cancer Screening Program, and immunochemical fecal occult blood test kit. A total of 2 additional artificial intelligence properties menus were added to allow user-ColorApp interaction: Analyze Your Status and ColorApp Calculator. The prototype has been published in the Google Play store. The mean usability score was 72 (SD 11.52), which indicates that ColorApp is a usable mobile app, and it can be used as a tool for community education on colorectal cancer.
CONCLUSIONS: ColorApp mobile app can be used as a user-friendly tool for community education on colorectal cancer.

Matched MeSH terms: Artificial Intelligence
Fulltext Diversified Talent Cultivation Mechanism of Early Childhood Physical Education Under the Full-Practice Concept - Oriented by Preschooler Mental Health and Intelligent Teaching

Wang N, Rahman MNBA, Daud MAKBM

Front Psychol, 2020;11:593063.
PMID: 33584429 DOI: 10.3389/fpsyg.2020.593063

In order to improve early childhood physical education, in this study, the talent cultivation mechanism for undergraduates was explored under the "full-practice" concept, oriented by preschooler mental health. First, from the perspective of preschooler psychology, the mechanisms of ability training and talent cultivation for undergraduates majoring in early childhood education were explored under the "full-practice" concept. Considering that the physical, psychological, and intellectual development of preschoolers shall follow the rules of physical education, and current early childhood education mainly focuses on intelligence education in China, early childhood physical education was analyzed further in this study. By investigating the undergraduate majors of early childhood education in Henan University, this study first summarized the current problems in early childhood education systems in universities. Secondly, combined with the form of physical education in kindergartens, strategies for talent cultivation and curriculum setting of early childhood physical education majors in colleges and universities were proposed. Finally, from the perspective of innovation and diversification of training forms, the cultivation of early childhood educators' physical education ability was analyzed at multiple levels and multiple objectives, and the integrated training system of early childhood education talents was constructed. The results show that, among all the courses for early childhood education major, compulsory courses account for 81.2% and optional courses account for 18.8%. In addition, a survey on undergraduates' attitudes toward the curriculum of their major demonstrates that 81.2% of the undergraduates thought that the range and content of practical courses should be increased, indicating that undergraduates majoring in early childhood education are dissatisfied with the current curriculum system, and they have an increased demand for practical courses. Correspondingly, it is vital to build and improve on the early childhood physical education. In terms of its talent cultivation, the "full-practice" concept helps combine theory with practice to improve the effectiveness of education and teaching, pushing forward the reform of the education system. Meanwhile, data- and intelligence-oriented teaching will become the new direction of modern sports development, as well as an important link for tracking and monitoring children's sports teaching in China. Through the continuous introduction of wearable artificial intelligence (AI) products, real-time monitoring of children's physical conditions can be realized, which helps improve the effectiveness of early childhood physical education.

Matched MeSH terms: Artificial Intelligence
Fulltext An eHealth Framework for Managing Pediatric Growth Disorders and Growth Hormone Therapy

Dimitri P, Fernandez-Luque L, Banerjee I, Bergadá I, Calliari LE, Dahlgren J, et al.

J Med Internet Res, 2021 05 20;23(5):e27446.
PMID: 34014174 DOI: 10.2196/27446

BACKGROUND: The use of technology to support health and health care has grown rapidly in the last decade across all ages and medical specialties. Newly developed eHealth tools are being implemented in long-term management of growth failure in children, a low prevalence pediatric endocrine disorder.
OBJECTIVE: Our objective was to create a framework that can guide future implementation and research on the use of eHealth tools to support patients with growth disorders who require growth hormone therapy.
METHODS: A total of 12 pediatric endocrinologists with experience in eHealth, from a wide geographical distribution, participated in a series of online discussions. We summarized the discussions of 3 workshops, conducted during 2020, on the use of eHealth in the management of growth disorders, which were structured to provide insights on existing challenges, opportunities, and solutions for the implementation of eHealth tools across the patient journey, from referral to the end of pediatric therapy.
RESULTS: A total of 815 responses were collected from 2 questionnaire-based activities covering referral and diagnosis of growth disorders, and subsequent growth hormone therapy stages of the patient pathway, relating to physicians, nurses, and patients, parents, or caregivers. We mapped the feedback from those discussions into a framework that we developed as a guide to integration of eHealth tools across the patient journey. Responses focused on improved clinical management, such as growth monitoring and automation of referral for early detection of growth disorders, which could trigger rapid evaluation and diagnosis. Patient support included the use of eHealth for enhanced patient and caregiver communication, better access to educational opportunities, and enhanced medical and psychological support during growth hormone therapy management. Given the potential availability of patient data from connected devices, artificial intelligence can be used to predict adherence and personalize patient support. Providing evidence to demonstrate the value and utility of eHealth tools will ensure that these tools are widely accepted, trusted, and used in clinical practice, but implementation issues (eg, adaptation to specific clinical settings) must be addressed.
CONCLUSIONS: The use of eHealth in growth hormone therapy has major potential to improve the management of growth disorders along the patient journey. Combining objective clinical information and patient adherence data is vital in supporting decision-making and the development of new eHealth tools. Involvement of clinicians and patients in the process of integrating such technologies into clinical practice is essential for implementation and developing evidence that eHealth tools can provide value across the patient pathway.

Matched MeSH terms: Artificial Intelligence
A hybrid prognostic model for oral cancer based on clinicopathologic and genomic markers

Chang SW, Merican AFMA, Rosnah Zain, Kareem SA

Sains Malaysiana, 2014;43:567-573.

There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic markers and to prove that the prognosis is better with both markers. The proposed hybrid model consisting of two stages, where stage one with ReliefF-GA feature selection method to find an optimal feature of subset and stage two with ANFIS classification to classify either the patients alive or dead after certain years of diagnosis. The proposed prognostic model was experimented on two groups of oral cancer dataset collected locally here in Malaysia, Group 1 with clinicopathologic markers only and Group 2 with both clinicopathologic and genomic markers. The results proved that the proposed model with optimum features selected is more accurate with the use of both clinicopathologic and genomic markers and outperformed the other methods of artificial neural network, support vector machine and logistic regression. This prognostic model is feasible to aid the clinicians in the decision support stage and to identify the high risk markers to better predict the survival rate for each oral cancer patient.

Matched MeSH terms: Artificial Intelligence

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