Research

14 pages, 1861 KiB

Open AccessArticle

Overall Survival Time Estimation for Epithelioid Peritoneal Mesothelioma Patients from Whole-Slide Images

by Kleanthis Marios Papadopoulos, Panagiotis Barmpoutis, Tania Stathaki, Vahan Kepenekian, Peggy Dartigues, Séverine Valmary-Degano, Claire Illac-Vauquelin, Gerlinde Avérous, Anne Chevallier, Marie-Hélène Laverriere, Laurent Villeneuve, Olivier Glehen, Sylvie Isaac, Juliette Hommell-Fontaine, Francois Ng Kee Kwong and Nazim Benzerdjeb

BioMedInformatics 2024, 4(1), 823-836; https://doi.org/10.3390/biomedinformatics4010046 - 13 Mar 2024

Viewed by 679

Abstract

Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior research has been conducted on image-based [...] Read more.

Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior research has been conducted on image-based survival analysis specifically for peritoneal mesothelioma. Prior studies performed statistical analysis to identify disease factors impacting patients’ survival time. Methods: Therefore, we introduce MPeMSupervisedSurv, a Convolutional Neural Network designed to predict the survival time of patients diagnosed with this disease. We subsequently perform patient stratification based on factors such as their Peritoneal Cancer Index and on whether patients received chemotherapy treatment. Results: MPeMSupervisedSurv demonstrates improvements over comparable methods. Using our proposed model, we performed patient stratification to assess the impact of clinical variables on survival time. Notably, the inclusion of information regarding adjuvant chemotherapy significantly enhances the model’s predictive prowess. Conversely, repeating the process for other factors did not yield significant performance improvements. Conclusions: Overall, MPeMSupervisedSurv is an effective neural network which can predict the survival time of peritoneal mesothelioma patients. Our findings also indicate that treatment by adjuvant chemotherapy could be a factor affecting survival time. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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23 pages, 2217 KiB

Open AccessArticle

Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

by Alae Eddine El Hmimdi, Zoï Kapoula and Vivien Sainte Fare Garnot

BioMedInformatics 2024, 4(1), 519-541; https://doi.org/10.3390/biomedinformatics4010029 - 14 Feb 2024

Viewed by 685

Abstract

Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs. control subjects; however, the data [...] Read more.

Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs. control subjects; however, the data size and the conditions of data acquisition were their most significant limitation. In the current study, we leverage a large dataset, containing 4243 time series of eye movement records from children across Europe. These datasets were derived from various tests such as saccade, vergence, and reading tasks. Furthermore, our methods were evaluated with realistic test data, including real-life biases such as noise, eye tracking misalignment, and similar pathologies among non-scholar difficulty classes. In addition, we present a novel convolutional neural network architecture, adapted to our time series classification problem, that is intended to generalize on a small annotated dataset and to handle a high-resolution signal (1024 point). Our architecture achieved a precision of 80.20% and a recall of 75.1%, when trained on the vergence dataset, and a precision of 77.2% and a recall of 77.5% when trained on the saccade dataset. Finally, we performed a comparison using our ML approach, a second architecture developed for a similar problem, and two other methods that we investigated that use deep learning algorithms to predict dyslexia. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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14 pages, 1526 KiB

Open AccessArticle

Ensemble Methods to Optimize Automated Text Classification in Avatar Therapy

by Alexandre Hudon, Kingsada Phraxayavong, Stéphane Potvin and Alexandre Dumais

BioMedInformatics 2024, 4(1), 423-436; https://doi.org/10.3390/biomedinformatics4010024 - 07 Feb 2024

Viewed by 1256

Abstract

Background: Psychotherapeutic approaches such as Avatar Therapy (AT) are novel therapeutic attempts to help patients diagnosed with treatment-resistant schizophrenia. Qualitative analyses of immersive sessions of AT have been undertaken to enhance and refine the existing interventions taking place in this therapy. To account [...] Read more.

Background: Psychotherapeutic approaches such as Avatar Therapy (AT) are novel therapeutic attempts to help patients diagnosed with treatment-resistant schizophrenia. Qualitative analyses of immersive sessions of AT have been undertaken to enhance and refine the existing interventions taking place in this therapy. To account for the time-consuming and costly nature and potential misclassification biases, prior implementation of a Linear Support Vector Classifier provided helpful insight. Single model implementation for text classification is often limited, especially for datasets containing imbalanced data. The main objective of this study is to evaluate the change in accuracy of automated text classification machine learning algorithms when using an ensemble approach for immersive session verbatims of AT. Methods: An ensemble model, comprising five machine learning algorithms, was implemented to conduct text classification for avatar and patient interactions. The models included in this study are: Multinomial Naïve Bayes, Linear Support Vector Classifier, Multi-layer perceptron classifier, XGBClassifier and the K-Nearest-Neighbor model. Accuracy, precision, recall and f1-score were compared for the individual classifiers and the ensemble model. Results: The ensemble model performed better than its individual counterparts for accuracy. Conclusion: Using an ensemble methodological approach, this methodology might be employed in future research to provide insight into the interactions being categorized and the therapeutical outcome of patients based on their experience with AT with optimal precision. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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26 pages, 8409 KiB

Open AccessArticle

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

by Joaquim Carreras, Yara Yukie Kikuti, Masashi Miyaoka, Saya Miyahara, Giovanna Roncador, Rifat Hamoudi and Naoya Nakamura

BioMedInformatics 2024, 4(1), 295-320; https://doi.org/10.3390/biomedinformatics4010017 - 26 Jan 2024

Cited by 1 | Viewed by 744

Abstract

Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin [...] Read more.

Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin molecular classification; Hans’ classification and derivates; and the Schmitz, Chapuy, Lacy, Reddy, and Sha models. This study introduced different machine learning techniques and their classification. Later, several machine learning techniques and artificial neural networks were used to predict the DLBCL subtypes with high accuracy (100–95%), including Germinal center B-cell like (GCB), Activated B-cell like (ABC), Molecular high-grade (MHG), and Unclassified (UNC), in the context of the data released by the REMoDL-B trial. In order of accuracy (MHG vs. others), the techniques were XGBoost tree (100%); random trees (99.9%); random forest (99.5%); and C5, Bayesian network, SVM, logistic regression, KNN algorithm, neural networks, LSVM, discriminant analysis, CHAID, C&R tree, tree-AS, Quest, and XGBoost linear (99.4–91.1%). The inputs (predictors) were all the genes of the array and a set of 28 genes related to DLBCL-Burkitt differential expression. In summary, artificial intelligence (AI) is a useful tool for predictive analytics using gene expression data. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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10 pages, 1271 KiB

Open AccessArticle

Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer

by Ping Lu, Zihao Wang, Hai Duong Ha Thi, Ho Bich Hai, VITAL Consortium, Louise Thwaites and David A. Clifton

BioMedInformatics 2024, 4(1), 285-294; https://doi.org/10.3390/biomedinformatics4010016 - 19 Jan 2024

Viewed by 810

Abstract

Tetanus, a life-threatening bacterial infection prevalent in low- and middle-income countries like Vietnam, impacts the nervous system, causing muscle stiffness and spasms. Severe tetanus often involves dysfunction of the autonomic nervous system (ANS). Timely detection and effective ANS dysfunction management require continuous vital [...] Read more.

Tetanus, a life-threatening bacterial infection prevalent in low- and middle-income countries like Vietnam, impacts the nervous system, causing muscle stiffness and spasms. Severe tetanus often involves dysfunction of the autonomic nervous system (ANS). Timely detection and effective ANS dysfunction management require continuous vital sign monitoring, traditionally performed using bedside monitors. However, wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative. While machine learning-based ECG analysis can aid in tetanus severity classification, existing methods are excessively time-consuming. Our previous studies have investigated the improvement of tetanus severity classification using ECG time series imaging. In this study, our aim is to explore an alternative method using ECG data without relying on time series imaging as an input, with the aim of achieving comparable or improved performance. To address this, we propose a novel approach using a 1D-Vision Transformer, a pioneering method for classifying tetanus severity by extracting crucial global information from 1D ECG signals. Compared to 1D-CNN, 2D-CNN, and 2D-CNN + Dual Attention, our model achieves better results, boasting an F1 score of 0.77 ± 0.06, precision of 0.70 ± 0. 09, recall of 0.89 ± 0.13, specificity of 0.78 ± 0.12, accuracy of 0.82 ± 0.06 and AUC of 0.84 ± 0.05. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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15 pages, 1208 KiB

Open AccessArticle

Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development

by Sarfaraz K. Niazi, Zamara Mariam and Rehan Z. Paracha

BioMedInformatics 2024, 4(1), 98-112; https://doi.org/10.3390/biomedinformatics4010007 - 08 Jan 2024

Viewed by 1224

Abstract

The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though [...] Read more.

The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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14 pages, 3802 KiB

Open AccessArticle

The Bioinformatics Identification of Potential Protein Glycosylation Genes Associated with a Glioma Stem Cell Signature

by Kazuya Tokumura, Koki Sadamori, Makoto Yoshimoto, Akane Tomizawa, Yuki Tanaka, Kazuya Fukasawa and Eiichi Hinoi

BioMedInformatics 2024, 4(1), 75-88; https://doi.org/10.3390/biomedinformatics4010005 - 01 Jan 2024

Viewed by 784

Abstract

Glioma stem cells (GSCs) contribute to the pathogenesis of glioblastoma (GBM), which is the most malignant form of glioma. The implications and underlying mechanisms of protein glycosylation in GSC phenotypes and GBM malignancy are not fully understood. The implication of protein glycosylation and [...] Read more.

Glioma stem cells (GSCs) contribute to the pathogenesis of glioblastoma (GBM), which is the most malignant form of glioma. The implications and underlying mechanisms of protein glycosylation in GSC phenotypes and GBM malignancy are not fully understood. The implication of protein glycosylation and the corresponding candidate genes on the stem cell properties of GSCs and poor clinical outcomes in GBM were investigated, using datasets from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Chinese Glioma Genome Atlas, accompanied by biological validation in vitro. N-linked glycosylation was significantly associated with GSC properties and the prognosis of GBM in the integrated bioinformatics analyses of clinical specimens. N-linked glycosylation was associated with the glioma grade, molecular biomarkers, and molecular subtypes. The expression levels of the asparagine-linked glycosylation (ALG) enzyme family, which is essential for the early steps in the biosynthesis of N-glycans, were prominently associated with GSC properties and poor survival in patients with GBM with high stem-cell properties. Finally, the oxidative phosphorylation pathway was primarily enriched in GSCs with a high expression of the ALG enzyme family. These findings suggest the role of N-linked glycosylation in the regulation of GSC phenotypes and GBM malignancy. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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26 pages, 1730 KiB

Open AccessArticle

Supporting the Demand on Mental Health Services with AI-Based Conversational Large Language Models (LLMs)

by Tin Lai, Yukun Shi, Zicong Du, Jiajie Wu, Ken Fu, Yichao Dou and Ziqi Wang

BioMedInformatics 2024, 4(1), 8-33; https://doi.org/10.3390/biomedinformatics4010002 - 22 Dec 2023

Viewed by 1853

Abstract

The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to [...] Read more.

The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging large language models (LLMs) for question answering in psychological consultation settings to ease the demand on mental health professions. Our framework combines pre-trained LLMs with real-world professional questions-and-answers (Q&A) from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, including human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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15 pages, 3794 KiB

Open AccessArticle

Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network

by Sumayyah Hasbullah, Mohd Soperi Mohd Zahid and Satria Mandala

BioMedInformatics 2023, 3(2), 478-492; https://doi.org/10.3390/biomedinformatics3020033 - 15 Jun 2023

Cited by 5 | Viewed by 1594

Abstract

Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, [...] Read more.

Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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12 pages, 6300 KiB

Open AccessArticle

Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi

by Ali Saber Amsalam, Ali Al-Naji, Ammar Yahya Daeef and Javaan Chahl

BioMedInformatics 2023, 3(2), 455-466; https://doi.org/10.3390/biomedinformatics3020031 - 13 Jun 2023

Cited by 1 | Viewed by 1704

Abstract

Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children [...] Read more.

Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis by visual examination, based on differences in the sides of the face, can be prone to errors and inaccuracies. The detection of FP using artificial intelligence through computer vision systems has become increasingly important. Deep learning is the best solution for detecting FP in real-time with high accuracy, saving patients time, effort, and cost. Therefore, this work proposes a real-time detection system for FP, and for determining the patient’s gender and age, using a Raspberry Pi device with a digital camera and a deep learning algorithm. The solution facilitates the diagnosis process for both the doctor and the patient, and it could be part of a medical assessment activity. This study used a dataset of 20,600 images, containing 19,000 normal images and 1600 FP images, to achieve an accuracy of 98%. Thus, the proposed system is a highly accurate and capable medical diagnostic tool for detecting FP. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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13 pages, 1323 KiB

Open AccessArticle

Ablefit: Development of an Advanced System for Rehabilitation

by Hugo Neves, Arménio Cruz, Rafael A. Bernardes, Remy Cardoso, Mónica Pimentel, Filipa Margarida Duque, Eliana Lopes, Daniela Veiga, Cândida Malça, Rúben Durães, Gustavo Corrente, Pedro Parreira, João Apóstolo and Vitor Parola

BioMedInformatics 2023, 3(1), 164-176; https://doi.org/10.3390/biomedinformatics3010012 - 01 Mar 2023

Cited by 1 | Viewed by 1504

Abstract

Bedridden patients risk presenting several problems caused by prolonged immobility, leading to a long recovery process. There is thus a need to develop solutions that ensure the implementation of physical rehabilitation programs in a controlled and interactive way. In this context, the ABLEFIT [...] Read more.

Bedridden patients risk presenting several problems caused by prolonged immobility, leading to a long recovery process. There is thus a need to develop solutions that ensure the implementation of physical rehabilitation programs in a controlled and interactive way. In this context, the ABLEFIT project aims to develop a medical device to physically rehabilitate bedridden patients with prolonged immobility. A partnership was established between the school of nursing, business enterprises and an engineering institute to develop a prototype. After creating the prototype, a pre-clinical experimental usability study was created using the user-centred multi-method approach (User and Human-Centered Design) to assess the device’s functionality, ergonomics and safety. The pre-clinical stage was initiated with a sample of 12 health professionals (that manipulated the device’s functionalities) and 10 end-users (who used the device). During the pre-clinical stage, the need to incorporate in the final version joint stabilizers was observed. Another important finding was the importance of the continuous monitorization of vital signs on Ablefit, namely, heart rate and SPO₂. Therefore, the development of the Ablefit system allows the monitoring of a set of variables and conditions inherent to immobility. At the same time, this device will be a dynamic solution (using gamification and simulation technologies) by generating personalized rehabilitation plans. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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19 pages, 4647 KiB

Open AccessArticle

Aedes Larva Detection Using Ensemble Learning to Prevent Dengue Endemic

by Md Shakhawat Hossain, Md Ezaz Raihan, Md Sakir Hossain, M. M. Mahbubul Syeed, Harunur Rashid and Md Shaheed Reza

BioMedInformatics 2022, 2(3), 405-423; https://doi.org/10.3390/biomedinformatics2030026 - 17 Aug 2022

Cited by 12 | Viewed by 8932

Abstract

Dengue endemicity has become regular in recent times across the world. The numbers of cases and deaths have been alarmingly increasing over the years. In addition to this, there are no direct medications or vaccines to treat this viral infection. Thus, monitoring and [...] Read more.

Dengue endemicity has become regular in recent times across the world. The numbers of cases and deaths have been alarmingly increasing over the years. In addition to this, there are no direct medications or vaccines to treat this viral infection. Thus, monitoring and controlling the carriers of this virus which are the Aedes mosquitoes become specially demanding to combat the endemicity, as killing all the mosquitoes regardless of their species would destroy ecosystems. The current approach requires collecting a larva sample from the hatching sites and, then, an expert entomologist manually examining it using a microscope in the laboratory to identify the Aedes vector. This is time-consuming, labor-intensive, subjective, and impractical. Several automated Aedes larvae detection systems have been proposed previously, but failed to achieve sufficient accuracy and reliability. We propose an automated system utilizing ensemble learning, which detects Aedes larvae effectively from a low-magnification image with an accuracy of over 99%. The proposed system outperformed all the previous methods with respect to accuracy. The practical usability of the system is also demonstrated. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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Review

Jump to: Research, Other

17 pages, 325 KiB

Open AccessReview

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

by Luís Ramalhete, Paula Almeida, Raquel Ferreira, Olga Abade, Cristiana Teixeira and Rúben Araújo

BioMedInformatics 2024, 4(1), 673-689; https://doi.org/10.3390/biomedinformatics4010037 - 01 Mar 2024

Viewed by 1017

Abstract

This review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against the backdrop of a significant donor organ shortage and the evolution of ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI and ML can [...] Read more.

This review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against the backdrop of a significant donor organ shortage and the evolution of ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI and ML can enhance the transplantation process, from donor selection to postoperative patient care. Our methodology involved a comprehensive review of current research, focusing on the application of AI and ML in various stages of KT. This included an analysis of donor–recipient matching, predictive modeling, and the improvement in postoperative care. The results indicated that AI and ML significantly improve the efficiency and success rates of KT. They aid in better donor–recipient matching, reduce organ rejection, and enhance postoperative monitoring and patient care. Predictive modeling, based on extensive data analysis, has been particularly effective in identifying suitable organ matches and anticipating postoperative complications. In conclusion, this review discusses the transformative impact of AI and ML in KT, offering more precise, personalized, and effective healthcare solutions. Their integration into this field addresses critical issues like organ shortages and post-transplant complications. However, the successful application of these technologies requires careful consideration of their ethical, privacy, and training aspects in healthcare settings. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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22 pages, 1127 KiB

Open AccessReview

A Comprehensive Survey of Digital Twins in Healthcare in the Era of Metaverse

by Muhammad Turab and Sonain Jamil

BioMedInformatics 2023, 3(3), 563-584; https://doi.org/10.3390/biomedinformatics3030039 - 21 Jul 2023

Cited by 9 | Viewed by 4636

Abstract

Digital twins (DTs) are becoming increasingly popular in various industries, and their potential for healthcare in the metaverse continues to attract attention. The metaverse is a virtual world where individuals interact with digital replicas of themselves and the environment. This paper focuses on [...] Read more.

Digital twins (DTs) are becoming increasingly popular in various industries, and their potential for healthcare in the metaverse continues to attract attention. The metaverse is a virtual world where individuals interact with digital replicas of themselves and the environment. This paper focuses on personalized and precise medicine and examines the current application of DTs in healthcare within the metaverse. Healthcare practitioners may use immersive virtual worlds to replicate medical scenarios, improve teaching experiences, and provide personalized care to patients. However, the integration of DTs in the metaverse poses technical, regulatory, and ethical challenges that need to be addressed, including data privacy, standards, and accessibility. Through this examination, we aim to provide insights into the transformative potential of DTs in healthcare within the metaverse and encourage further research and development in this exciting domain. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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Other

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12 pages, 6504 KiB

Open AccessProject Report

Investigating the Effectiveness of an IMU Portable Gait Analysis Device: An Application for Parkinson’s Disease Management

by Nikos Tsotsolas, Eleni Koutsouraki, Aspasia Antonakaki, Stefanos Pizanias, Marios Kounelis, Dimitrios D. Piromalis, Dimitrios P. Kolovos, Christos Kokkotis, Themistoklis Tsatalas, George Bellis, Dimitrios Tsaopoulos, Paris Papaggelos, George Sidiropoulos and Giannis Giakas

BioMedInformatics 2024, 4(2), 1085-1096; https://doi.org/10.3390/biomedinformatics4020061 - 10 Apr 2024

Viewed by 290

Abstract

As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth [...] Read more.

As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth analysis of the gait characteristics, while the project CPWATCHER aims to assess the quality of hand movement in cerebral palsy patients. The device was mainly developed to serve the first project with additional offline processing, including machine learning algorithms that could potentially be used for the second aim. A key feature of the device is its small size (36 mm × 46 mm × 16 mm, weight: 14 g), which was designed to meet specific requirements in terms of device consumption restrictions due to the small size of the battery and the need for autonomous operation for more than ten hours. This research work describes, on the one hand, the new device with an emphasis on its functions, and on the other hand, its connection with a web platform for reading and processing data from the devices placed on patients’ feet to record the gait characteristics of patients on a continuous basis. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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12 pages, 921 KiB

Open AccessCase Report

Avatar Intervention for Cannabis Use Disorder in a Patient with Schizoaffective Disorder: A Case Report

by Sabrina Giguère, Laura Dellazizzo, Mélissa Beaudoin, Marie-Andrée Lapierre, Marie Villeneuve, Kingsada Phraxayavong, Stéphane Potvin and Alexandre Dumais

BioMedInformatics 2023, 3(4), 1112-1123; https://doi.org/10.3390/biomedinformatics3040067 - 06 Dec 2023

Viewed by 710

Abstract

Considering the harmful effects of cannabis on individuals with a severe mental disorder and the limited effectiveness of current interventions, this case report showcases the beneficial results of a 10-session Avatar intervention for cannabis use disorder (CUD) on a polysubstance user with a [...] Read more.

Considering the harmful effects of cannabis on individuals with a severe mental disorder and the limited effectiveness of current interventions, this case report showcases the beneficial results of a 10-session Avatar intervention for cannabis use disorder (CUD) on a polysubstance user with a comorbid schizoaffective disorder. Virtual reality allowed the creation of an Avatar representing a person significantly related to the patient’s drug use. Avatar intervention for CUD aims to combine exposure, relational, and cognitive behavioral therapies while practicing real-life situations and learning how to manage negative emotions and cravings. Throughout therapy and later on, Mr. C managed to maintain abstinence from all substances. Also, an improvement in the severity of CUD, as well as a greater motivation to change consumption, was observed after therapy. As observed by his mother, his psychiatrist, and himself, the benefits of Avatar intervention for CUD extended to other spheres of his life. The drastic results observed in this patient could be promising as an alternative to the current treatment available for people with a dual diagnosis of cannabis use disorder and psychotic disorder, which generally lack effectiveness. A single-blind randomized control trial comparing the treatment with a classical intervention in a larger sample is currently underway to evaluate whether the results are reproducible on a larger sample. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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10 pages, 1208 KiB

Open AccessData Descriptor

NJN: A Dataset for the Normal and Jaundiced Newborns

by Ahmad Yaseen Abdulrazzak, Saleem Latif Mohammed and Ali Al-Naji

BioMedInformatics 2023, 3(3), 543-552; https://doi.org/10.3390/biomedinformatics3030037 - 05 Jul 2023

Cited by 1 | Viewed by 1898

Abstract

Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood sample collection, which can inflict [...] Read more.

Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood sample collection, which can inflict pain and distress on the patient, and may give rise to additional complications. Alternatively, a non-invasive method using image-processing techniques and implementing kNN, Random Forest, and XGBoost machine learning algorithms as a classifier can be employed to diagnose jaundice, necessitating a comprehensive database of infant images to achieve a diagnosis with high accuracy. This data article presents the NJN collection, a repository of newborn images encompassing diverse birthweights and skin tones, spanning an age range of 2 to 8 days. The dataset is accompanied by an Excel sheet file in CSV format containing the RGB and YCrCb channel values, as well as the status of each sample. The dataset and associated resources are openly accessible at Zenodo website. Moreover, the Python code for data testing utilizing various AI techniques is provided. Consequently, this article offers an unparalleled resource for AI researchers, enabling them to train their AI systems and develop algorithms that can assist neonatal intensive care unit (NICU) healthcare specialists in monitoring neonates while facilitating the fast, real-time, non-invasive, and accurate diagnosis of jaundice. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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13 pages, 2011 KiB

Open AccessPerspective

AlphaFold2 Update and Perspectives

by Sébastien Tourlet, Ragousandirane Radjasandirane, Julien Diharce and Alexandre G. de Brevern

BioMedInformatics 2023, 3(2), 378-390; https://doi.org/10.3390/biomedinformatics3020025 - 09 May 2023

Cited by 7 | Viewed by 3017

Abstract

Access to the three-dimensional (3D) structural information of macromolecules is of major interest in both fundamental and applied research. Obtaining this experimental data can be complex, time consuming, and costly. Therefore, in silico computational approaches are an alternative of interest, and sometimes present [...] Read more.

Access to the three-dimensional (3D) structural information of macromolecules is of major interest in both fundamental and applied research. Obtaining this experimental data can be complex, time consuming, and costly. Therefore, in silico computational approaches are an alternative of interest, and sometimes present a unique option. In this context, the Protein Structure Prediction method AlphaFold2 represented a revolutionary advance in structural bioinformatics. Named method of the year in 2021, and widely distributed by DeepMind and EBI, it was thought at this time that protein-folding issues had been resolved. However, the reality is slightly more complex. Due to a lack of input experimental data, related to crystallographic challenges, some targets have remained highly challenging or not feasible. This perspective exercise, dedicated to a non-expert audience, discusses and correctly places AlphaFold2 methodology in its context and, above all, highlights its use, limitations, and opportunities. After a review of the interest in the 3D structure and of the previous methods used in the field, AF2 is brought into its historical context. Its spatial interests are detailed before presenting precise quantifications showing some limitations of this approach and finishing with the perspectives in the field. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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27 pages, 2261 KiB

Open AccessSystematic Review

A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals

by Jolly Ehiabhi and Haifeng Wang

BioMedInformatics 2023, 3(1), 193-219; https://doi.org/10.3390/biomedinformatics3010014 - 01 Mar 2023

Cited by 4 | Viewed by 4303

Abstract

With the increase in biosensors and data collection devices in the healthcare industry, artificial intelligence and machine learning have attracted much attention in recent years. In this study, we offered a comprehensive review of the current trends and the state-of-the-art in mental health [...] Read more.

With the increase in biosensors and data collection devices in the healthcare industry, artificial intelligence and machine learning have attracted much attention in recent years. In this study, we offered a comprehensive review of the current trends and the state-of-the-art in mental health analysis as well as the application of machine-learning techniques for analyzing multi-variate/multi-channel multi-modal biometric signals.This study reviewed the predominant mental-health-related biosensors, including polysomnography (PSG), electroencephalogram (EEG), electro-oculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). We also described the processes used for data acquisition, data-cleaning, feature extraction, machine-learning modeling, and performance evaluation. This review showed that support-vector-machine and deep-learning techniques have been well studied, to date.After reviewing over 200 papers, we also discussed the current challenges and opportunities in this field. Full article

(This article belongs to the Special Issue Deep Learning Methods and Application for Bioinformatics and Healthcare)

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