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23 pages, 4537 KiB

Open AccessEditor’s ChoiceArticle

A Machine-Learning-Based Motor and Cognitive Assessment Tool Using In-Game Data from the GAME2AWE Platform

by Michail Danousis and Christos Goumopoulos

Informatics 2023, 10(3), 59; https://doi.org/10.3390/informatics10030059 - 9 Jul 2023

Viewed by 2403

Abstract

With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining [...] Read more.

With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining functional independence and improving overall well-being. This paper explores the potential of the GAME2AWE platform in assessing the motor and cognitive condition of seniors based on their in-game performance data. The proposed methodology involves developing machine learning models to explore the predictive power of features that are derived from the data collected during gameplay on the GAME2AWE platform. Through a study involving fifteen elderly participants, we demonstrate that utilizing in-game data can achieve a high classification performance when predicting the motor and cognitive states. Various machine learning techniques were used but Random Forest outperformed the other models, achieving a classification accuracy ranging from 93.6% for cognitive screening to 95.6% for motor assessment. These results highlight the potential of using exergames within a technology-rich environment as an effective means of capturing the health status of seniors. This approach opens up new possibilities for objective and non-invasive health assessment, facilitating early detections and interventions to improve the well-being of seniors. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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10 pages, 2139 KiB

Open AccessArticle

Classification of Benign and Malignant Renal Tumors Based on CT Scans and Clinical Data Using Machine Learning Methods

by Jie Xu, Xing He, Wei Shao, Jiang Bian and Russell Terry

Informatics 2023, 10(3), 55; https://doi.org/10.3390/informatics10030055 - 3 Jul 2023

Cited by 1 | Viewed by 3332

Abstract

Up to 20% of renal masses ≤4 cm is found to be benign at the time of surgical excision, raising concern for overtreatment. However, the risk of malignancy is currently unable to be accurately predicted prior to surgery using imaging alone. The objective [...] Read more.

Up to 20% of renal masses ≤4 cm is found to be benign at the time of surgical excision, raising concern for overtreatment. However, the risk of malignancy is currently unable to be accurately predicted prior to surgery using imaging alone. The objective of this study is to propose a machine learning (ML) framework for pre-operative renal tumor classification using readily available clinical and CT imaging data. We tested both traditional ML methods (i.e., XGBoost, random forest (RF)) and deep learning (DL) methods (i.e., multilayer perceptron (MLP), 3D convolutional neural network (3DCNN)) to build the classification model. We discovered that the combination of clinical and radiomics features produced the best results (i.e., AUC [95% CI] of 0.719 [0.712–0.726], a precision [95% CI] of 0.976 [0.975–0.978], a recall [95% CI] of 0.683 [0.675–0.691], and a specificity [95% CI] of 0.827 [0.817–0.837]). Our analysis revealed that employing ML models with CT scans and clinical data holds promise for classifying the risk of renal malignancy. Future work should focus on externally validating the proposed model and features to better support clinical decision-making in renal cancer diagnosis. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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13 pages, 1378 KiB

Open AccessFeature PaperArticle

Predicting the Risk of Alzheimer’s Disease and Related Dementia in Patients with Mild Cognitive Impairment Using a Semi-Competing Risk Approach

by Zhaoyi Chen, Yuchen Yang, Dazheng Zhang, Jingchuan Guo, Yi Guo, Xia Hu, Yong Chen and Jiang Bian

Informatics 2023, 10(2), 46; https://doi.org/10.3390/informatics10020046 - 30 May 2023

Viewed by 2355

Abstract

Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) [...] Read more.

Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) to mild cognitive impairment (MCI) due to AD (i.e., mild symptoms but not interfere with daily activities), followed by increasing severity of dementia due to AD. Early detection and prediction models for the transition of MCI to AD/ADRD are needed, and efforts have been made to build predictions of MCI conversion to AD/ADRD. However, most existing studies developing such prediction models did not consider the competing risks of death, which may result in biased risk estimates. In this study, we aim to develop a prediction model for AD/ADRD among patients with MCI considering the competing risks of death using a semi-competing risk approach. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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16 pages, 8289 KiB

Open AccessEditor’s ChoiceArticle

Development and Internal Validation of an Interpretable Machine Learning Model to Predict Readmissions in a United States Healthcare System

by Amanda L. Luo, Akshay Ravi, Simone Arvisais-Anhalt, Anoop N. Muniyappa, Xinran Liu and Shan Wang

Informatics 2023, 10(2), 33; https://doi.org/10.3390/informatics10020033 - 27 Mar 2023

Viewed by 2842

Abstract

(1) One in four hospital readmissions is potentially preventable. Machine learning (ML) models have been developed to predict hospital readmissions and risk-stratify patients, but thus far they have been limited in clinical applicability, timeliness, and generalizability. (2) Methods: Using deidentified clinical data from [...] Read more.

(1) One in four hospital readmissions is potentially preventable. Machine learning (ML) models have been developed to predict hospital readmissions and risk-stratify patients, but thus far they have been limited in clinical applicability, timeliness, and generalizability. (2) Methods: Using deidentified clinical data from the University of California, San Francisco (UCSF) between January 2016 and November 2021, we developed and compared four supervised ML models (logistic regression, random forest, gradient boosting, and XGBoost) to predict 30-day readmissions for adults admitted to a UCSF hospital. (3) Results: Of 147,358 inpatient encounters, 20,747 (13.9%) patients were readmitted within 30 days of discharge. The final model selected was XGBoost, which had an area under the receiver operating characteristic curve of 0.783 and an area under the precision-recall curve of 0.434. The most important features by Shapley Additive Explanations were days since last admission, discharge department, and inpatient length of stay. (4) Conclusions: We developed and internally validated a supervised ML model to predict 30-day readmissions in a US-based healthcare system. This model has several advantages including state-of-the-art performance metrics, the use of clinical data, the use of features available within 24 h of discharge, and generalizability to multiple disease states. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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12 pages, 291 KiB

Open AccessArticle

Effectiveness of Telemedicine in Diabetes Management: A Retrospective Study in an Urban Medically Underserved Population Area (UMUPA)

by Lisa Ariellah Ward, Gulzar H. Shah, Jeffery A. Jones, Linda Kimsey and Hani Samawi

Informatics 2023, 10(1), 16; https://doi.org/10.3390/informatics10010016 - 29 Jan 2023

Cited by 3 | Viewed by 3725

Abstract

This paper examines the efficacy of telemedicine (TM) technology compared to traditional face-to-face (F2F) visits as an alternative healthcare delivery service for managing diabetes in populations residing in urban medically underserved areas (UMUPAs). Retrospective electronic patient health records (ePHR) with type 2 diabetes [...] Read more.

This paper examines the efficacy of telemedicine (TM) technology compared to traditional face-to-face (F2F) visits as an alternative healthcare delivery service for managing diabetes in populations residing in urban medically underserved areas (UMUPAs). Retrospective electronic patient health records (ePHR) with type 2 diabetes mellitus (T2DM) were examined from 1 January 2019 to 30 June 2021. Multiple linear regression models indicated that T2DM patients with uncontrolled diabetes utilizing TM were similar to traditional visits in lowering hemoglobin (HbA1c) levels. The healthcare service type significantly predicted HbA1c % values, as the regression coefficient for TM (vs. F2F) showed a significant negative association (B = −0.339, p < 0.001), suggesting that patients using TM were likely to have 0.34 lower HbA1c % values on average when compared with F2F visits. The regression coefficient for female (vs. male) gender showed a positive association (B = 0.190, p < 0.034), with HbA1c % levels showing that female patients had 0.19 higher HbA1c levels than males. Age (B = −0.026, p < 0.001) was a significant predictor of HbA1c % levels, with 0.026 lower HbA1c % levels for each year’s increase in age. Black adults (B = 0.888, p < 0.001), on average, were more likely to have 0.888 higher HbA1c % levels when compared with White adults. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

30 pages, 4221 KiB

Open AccessEditor’s ChoiceArticle

Development of a Chatbot for Pregnant Women on a Posyandu Application in Indonesia: From Qualitative Approach to Decision Tree Method

by Indriana Widya Puspitasari, Fedri Ruluwedrata Rinawan, Wanda Gusdya Purnama, Hadi Susiarno and Ari Indra Susanti

Informatics 2022, 9(4), 88; https://doi.org/10.3390/informatics9040088 - 27 Oct 2022

Cited by 7 | Viewed by 7023

Abstract

With the widespread application of digital healthcare, mobile health (mHealth) services are also developing in maternal and child health, primarily through community-based services, such as Posyandu in Indonesia. Patients need media for consultation and decision-making, while health workers are constrained in responding quickly. [...] Read more.

With the widespread application of digital healthcare, mobile health (mHealth) services are also developing in maternal and child health, primarily through community-based services, such as Posyandu in Indonesia. Patients need media for consultation and decision-making, while health workers are constrained in responding quickly. This study aimed to obtain information from pregnant women and midwives in developing a decision tree model as material for building a semi-automated chatbot. Using an exploratory qualitative approach, semi-structured interviews were conducted through focus group discussions (FGD) with pregnant women (n = 10) and midwives (n = 12) in March 2022. The results showed 38 codes, 15 categories, and 7 subthemes that generated 3 major themes: maternal health education, information on maternal health services, and health monitoring. The decision tree method was applied from these themes based on the needs of users, evidence, and expert sources to ensure quality. In summary, the need to use a semi-automated chatbot can be applied to education about maternal health and monitoring, where severe cases should be provided with non-automated communication with midwives. Applying the decision tree method ensured quality content, supported a clinical decision, and assisted in early detection. Furthermore, future research needs to measure user evaluation. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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18 pages, 2920 KiB

Open AccessEditor’s ChoiceArticle

Classification of Malaria Using Object Detection Models

by Padmini Krishnadas, Krishnaraj Chadaga, Niranjana Sampathila, Santhosha Rao, Swathi K. S. and Srikanth Prabhu

Informatics 2022, 9(4), 76; https://doi.org/10.3390/informatics9040076 - 27 Sep 2022

Cited by 36 | Viewed by 11684

Abstract

Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often [...] Read more.

Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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15 pages, 866 KiB

Open AccessArticle

Where Is My Mind (Looking at)? A Study of the EEG–Visual Attention Relationship

by Victor Delvigne, Noé Tits, Luca La Fisca, Nathan Hubens, Antoine Maiorca, Hazem Wannous, Thierry Dutoit and Jean-Philippe Vandeborre

Informatics 2022, 9(1), 26; https://doi.org/10.3390/informatics9010026 - 9 Mar 2022

Cited by 1 | Viewed by 4589

Abstract

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, [...] Read more.

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. Image-based saliency estimation being participant independent, the estimation from EEG could take into account the subject specificity. For this purpose, a set of signals has been recorded, and different models have been developed to study the relationship between visual attention and brain activity. The results are encouraging and comparable with other approaches estimating attention with other modalities. Being able to predict a visual saliency map from EEG could help in research studying the relationship between brain activity and visual attention. It could also help in various applications: vigilance assessment during driving, neuromarketing, and also in the help for the diagnosis and treatment of visual attention-related diseases. For the sake of reproducibility, the codes and dataset considered in this paper have been made publicly available to promote research in the field. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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14 pages, 1747 KiB

Open AccessArticle

Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

by Vidhya V, U. Raghavendra, Anjan Gudigar, Praneet Kasula, Yashas Chakole, Ajay Hegde, Girish Menon R, Chui Ping Ooi, Edward J. Ciaccio and U. Rajendra Acharya

Informatics 2022, 9(1), 4; https://doi.org/10.3390/informatics9010004 - 10 Jan 2022

Cited by 4 | Viewed by 4043

Abstract

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can [...] Read more.

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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Review

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14 pages, 3835 KiB

Open AccessReview

A Survey on Computer-Aided Intelligent Methods to Identify and Classify Skin Cancer

by Jacinth Poornima Jeyakumar, Anitha Jude, Asha Gnana Priya and Jude Hemanth

Informatics 2022, 9(4), 99; https://doi.org/10.3390/informatics9040099 - 11 Dec 2022

Cited by 2 | Viewed by 2901

Abstract

Melanoma is one of the skin cancer types that is more dangerous to human society. It easily spreads to other parts of the human body. An early diagnosis is necessary for a higher survival rate. Computer-aided diagnosis (CAD) is suitable for providing precise [...] Read more.

Melanoma is one of the skin cancer types that is more dangerous to human society. It easily spreads to other parts of the human body. An early diagnosis is necessary for a higher survival rate. Computer-aided diagnosis (CAD) is suitable for providing precise findings before the critical stage. The computer-aided diagnostic process includes preprocessing, segmentation, feature extraction, and classification. This study discusses the advantages and disadvantages of various computer-aided algorithms. It also discusses the current approaches, problems, and various types of datasets for skin images. Information about possible future works is also highlighted in this paper. The inferences derived from this survey will be useful for researchers carrying out research in skin cancer image analysis. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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17 pages, 1724 KiB

Open AccessReview

Intelligent Remote Photoplethysmography-Based Methods for Heart Rate Estimation from Face Videos: A Survey

by Smera Premkumar and Duraisamy Jude Hemanth

Informatics 2022, 9(3), 57; https://doi.org/10.3390/informatics9030057 - 7 Aug 2022

Cited by 15 | Viewed by 5953

Abstract

Over the last few years, a rich amount of research has been conducted on remote vital sign monitoring of the human body. Remote photoplethysmography (rPPG) is a camera-based, unobtrusive technology that allows continuous monitoring of changes in vital signs and thereby helps to [...] Read more.

Over the last few years, a rich amount of research has been conducted on remote vital sign monitoring of the human body. Remote photoplethysmography (rPPG) is a camera-based, unobtrusive technology that allows continuous monitoring of changes in vital signs and thereby helps to diagnose and treat diseases earlier in an effective manner. Recent advances in computer vision and its extensive applications have led to rPPG being in high demand. This paper specifically presents a survey on different remote photoplethysmography methods and investigates all facets of heart rate analysis. We explore the investigation of the challenges of the video-based rPPG method and extend it to the recent advancements in the literature. We discuss the gap within the literature and suggestions for future directions. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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19 pages, 3245 KiB

Open AccessReview

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

by Anjan Gudigar, Raghavendra U., Jyothi Samanth, Akhila Vasudeva, Ashwal A. J., Krishnananda Nayak, Ru-San Tan, Edward J. Ciaccio, Chui Ping Ooi, Prabal Datta Barua, Filippo Molinari and U. Rajendra Acharya

Informatics 2022, 9(2), 34; https://doi.org/10.3390/informatics9020034 - 18 Apr 2022

Cited by 7 | Viewed by 10633

Abstract

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) [...] Read more.

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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