BioMedInformatics

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12 pages, 6854 KiB

Open AccessArticle

Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

by Aravinthan Sankar, Kunal Chaturvedi, Al-Akhir Nayan, Mohammad Hesam Hesamian, Ali Braytee and Mukesh Prasad

BioMedInformatics 2024, 4(2), 1059-1070; https://doi.org/10.3390/biomedinformatics4020059 - 09 Apr 2024

Viewed by 493

Abstract

Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy [...] Read more.

Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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28 pages, 2543 KiB

Open AccessArticle

Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning

by Shisir Ruwali, Shawhin Talebi, Ashen Fernando, Lakitha O. H. Wijeratne, John Waczak, Prabuddha M. H. Dewage, David J. Lary, John Sadler, Tatiana Lary, Matthew Lary and Adam Aker

BioMedInformatics 2024, 4(2), 1019-1046; https://doi.org/10.3390/biomedinformatics4020057 - 03 Apr 2024

Viewed by 622

Abstract

Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM₁ and PM_2.5, carbon dioxide (CO₂), nitrogen dioxide (NO₂), and nitric oxide (NO) are exemplars of the [...] Read more.

Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM₁ and PM_2.5, carbon dioxide (CO₂), nitrogen dioxide (NO₂), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM₁, PM_2.5, CO₂, NO₂, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM₁, PM_2.5, and CO₂ using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R²) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO₂ and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM₁, PM_2.5, and CO₂ with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO₂ and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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12 pages, 2906 KiB

Open AccessArticle

Predictive Analysis of Endoscope Demand in Otolaryngology Outpatient Settings

by David Lanier, Cristie Roush, Gwendolyn Young and Sara Masoud

BioMedInformatics 2024, 4(1), 721-732; https://doi.org/10.3390/biomedinformatics4010040 - 02 Mar 2024

Cited by 1 | Viewed by 465

Abstract

Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for [...] Read more.

Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for predicting and managing clinical demand for flexible endoscopes. The HLD disinfection process is time-consuming and requires specialized training and competency to be performed safely. Solely depending on human expertise for predicting the flexible endoscope demands is unreliable and produced a concern of an inadequate supply of devices available for diagnostic purposes. Method: The demand for flexible endoscopes for future patient visits has not been well studied but can be modeled based on patients’ historical information, provider, and other visit-related factors. Such factors are available to the clinic before the visit. Binary classifiers can be used to help inform the sterile processing department of reprocessing needs days or weeks earlier for each patient. Results: Among all our trained models, Logistic Regression reports an average AUC ROC score of 89% and accuracy of 80%. Conclusion: The proposed framework not only significantly reduces the reprocessing efforts in terms of time spent on communication, cleaning, scheduling, and transferring scopes, but also helps to improve patient safety by reducing the exposure risk to potential infections. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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15 pages, 3229 KiB

Open AccessArticle

Real-Time Jaundice Detection in Neonates Based on Machine Learning Models

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

BioMedInformatics 2024, 4(1), 623-637; https://doi.org/10.3390/biomedinformatics4010034 - 24 Feb 2024

Viewed by 761

Abstract

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose [...] Read more.

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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17 pages, 1062 KiB

Open AccessArticle

Non-Contact Blood Pressure Estimation Using Forehead and Palm Infrared Video

by Thomas Stogiannopoulos and Nikolaos Mitianoudis

BioMedInformatics 2024, 4(1), 437-453; https://doi.org/10.3390/biomedinformatics4010025 - 07 Feb 2024

Viewed by 584

Abstract

This study investigates the potential of low-cost infrared cameras for non-contact monitoring of blood pressure (BP) in individuals with fragile health, particularly the elderly. Previous research has shown success in developing non-contact BP monitoring using RGB cameras. In this study, the Eulerian Video [...] Read more.

This study investigates the potential of low-cost infrared cameras for non-contact monitoring of blood pressure (BP) in individuals with fragile health, particularly the elderly. Previous research has shown success in developing non-contact BP monitoring using RGB cameras. In this study, the Eulerian Video Magnification (EVM) technique is employed to enhance minor variations in skin pixel intensity in specific facial regions captured by an infrared camera from the forehead and palm. The primary focus of this study is to explore the possibility of using infrared cameras for non-contact BP monitoring under low-light or night-time conditions. We have successfully shown that by employing a series of straightforward signal processing techniques and regression analysis, we were able to achieve commendable outcomes in our experimental setup. Specifically, we were able to surpass the stringent accuracy standards set forth by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) protocol. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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19 pages, 3789 KiB

Open AccessArticle

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

by Osama A. Omer, Mostafa Salah, Ammar M. Hassan, Mohamed Abdel-Nasser, Norihiro Sugita and Yoshifumi Saijo

BioMedInformatics 2024, 4(1), 139-157; https://doi.org/10.3390/biomedinformatics4010010 - 09 Jan 2024

Viewed by 751

Abstract

One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, [...] Read more.

One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, machine/deep learning has been leveraged for learning a BP from photoplethysmography (PPG) signals. Hence, continuous BP monitoring can be introduced, based on simple wearable contact sensors or even remotely sensed from a proper camera away from the clinical setup. However, the available training dataset imposes many limitations besides the other difficulties related to the PPG time series as high-dimensional data. This work presents beat-by-beat continuous PPG-based BP monitoring while accounting for the aforementioned limitations. For a better exploration of beats’ features, we propose to use wavelet scattering transform as a better descriptive domain to cope with the limitation of the training dataset and to help the deep learning network accurately learn the relationship between the morphological shapes of PPG beats and the BP. A long short-term memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over other domains. The learning scenarios are carried out on a beat basis where the input corresponding PPG beat is used for predicting BP in two scenarios; (1) Beat-by-beat arterial blood pressure (ABP) estimation, and (2) Beat-by-beat estimation of the systolic and diastolic blood pressure values. Different transformations are used to extract the features of the PPG beats in different domains including time, discrete cosine transform (DCT), discrete wavelet transform (DWT), and wavelet scattering transform (WST) domains. The simulation results show that using the WST domain outperforms the other domains in the sense of root mean square error (RMSE) and mean absolute error (MAE) for both of the suggested two scenarios. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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25 pages, 8083 KiB

Open AccessArticle

Integrative Meta-Analysis during Induced Pluripotent Stem Cell Reprogramming Reveals Conserved Networks and Chromatin Accessibility Signatures in Human and Mouse

by Chloe S. Thangavelu and Trina M. Norden-Krichmar

BioMedInformatics 2023, 3(4), 1015-1039; https://doi.org/10.3390/biomedinformatics3040061 - 06 Nov 2023

Cited by 1 | Viewed by 991

Abstract

iPSC reprogramming involves dynamic changes in chromatin accessibility necessary for the conversion of somatic cells into induced pluripotent stem cells (iPSCs). IPSCs can be used to generate a wide range of cells to potentially replace damaged cells in a patient without the threat [...] Read more.

iPSC reprogramming involves dynamic changes in chromatin accessibility necessary for the conversion of somatic cells into induced pluripotent stem cells (iPSCs). IPSCs can be used to generate a wide range of cells to potentially replace damaged cells in a patient without the threat of immune rejection; however, efficiently reprogramming cells for medical applications remains a challenge, particularly in human cells. Here, we conducted a cross-species meta-analysis to identify conserved and species-specific differences in regulatory patterns during reprogramming. Chromatin accessibility and transcriptional data as fibroblasts transitioned to iPSCs were obtained from the publicly available Gene Expression Omnibus (GEO) database and integrated to generate time-resolved regulatory networks during cellular reprogramming. We observed consistent and conserved trends between the species in the chromatin accessibility signatures as cells transitioned from fibroblasts into iPSCs, indicating distal control of genes associated with pluripotency by master reprogramming regulators. Multi-omic integration showed key network changes across reprogramming states, revealing regulatory relationships between chromatin regulators, enhancers, transcription factors, and target genes that result in the silencing of the somatic transcription program and activation of the pluripotency gene regulatory network. This integrative analysis revealed distinct network changes between timepoints and leveraged multi-omics to gain novel insights into the regulatory mechanisms underlying reprogramming. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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23 pages, 1636 KiB

Open AccessArticle

Enhancing Semantic Web Technologies Using Lexical Auditing Techniques for Quality Assurance of Biomedical Ontologies

by Rashmi Burse, Michela Bertolotto and Gavin McArdle

BioMedInformatics 2023, 3(4), 962-984; https://doi.org/10.3390/biomedinformatics3040059 - 01 Nov 2023

Viewed by 684

Abstract

Semantic web technologies (SWT) represent data in a format that is easier for machines to understand. Validating the knowledge in data graphs created using SWT is critical to ensure that the axioms accurately represent the so-called “real” world. However, data graph validation is [...] Read more.

Semantic web technologies (SWT) represent data in a format that is easier for machines to understand. Validating the knowledge in data graphs created using SWT is critical to ensure that the axioms accurately represent the so-called “real” world. However, data graph validation is a significant challenge in the semantic web domain. The Shapes Constraint Language (SHACL) is the latest W3C standard developed with the goal of validating data-graphs. SHACL (pronounced as shackle) is a relatively new standard and hitherto has predominantly been employed to validate generic data graphs like WikiData and DBPedia. In generic data graphs, the name of a class does not affect the shape of a class, but this is not the case with biomedical ontology data graphs. The shapes of classes in biomedical ontology data graphs are highly influenced by the names of the classes, and the SHACL shape creation methods developed for generic data graphs fail to consider this characteristic difference. Thus, the existing SHACL shape creation methods do not perform well for domain-specific biomedical ontology data graphs. Maintaining the quality of biomedical ontology data graphs is crucial to ensure accurate analysis in safety-critical applications like Electronic Health Record (EHR) systems referencing such data graphs. Thus, in this work, we present a novel method to create enhanced SHACL shapes that consider the aforementioned characteristic difference to better validate biomedical ontology data graphs. We leverage the knowledge available from lexical auditing techniques for biomedical ontologies and incorporate this knowledge to create smart SHACL shapes. We also create SHACL shapes (baseline SHACL graph) without incorporating the lexical knowledge of the class names, as is performed by existing methods, and compare the performance of our enhanced SHACL shapes with the baseline SHACL shapes. The results demonstrate that the enhanced SHACL shapes augmented with lexical knowledge of the class names identified 176 violations which the baseline SHACL shapes, void of this lexical knowledge, failed to detect. Thus, the enhanced SHACL shapes presented in this work significantly improve the validation performance of biomedical ontology data graphs, thereby reducing the errors present in such data graphs and ensuring safe use in the life-critical applications referencing them. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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11 pages, 632 KiB

Open AccessArticle

Clinical and Demographic Attributes of Patients with Diabetes Associated with the Utilization of Telemedicine in an Urban Medically Underserved Population Area

by Lisa Ariellah Ward, Gulzar H. Shah and Kristie C. Waterfield

BioMedInformatics 2023, 3(3), 605-615; https://doi.org/10.3390/biomedinformatics3030041 - 01 Aug 2023

Viewed by 955

Abstract

Marginalized populations often experience health disparities due to the significant obstacles to care associated with social, economic, and environmental inequities. When compared with advantaged social groups, these populations frequently experience increased risks, poorer health outcomes, and reduced quality of life (QoL). This research [...] Read more.

Marginalized populations often experience health disparities due to the significant obstacles to care associated with social, economic, and environmental inequities. When compared with advantaged social groups, these populations frequently experience increased risks, poorer health outcomes, and reduced quality of life (QoL). This research examines the clinical and demographic characteristics—age, gender, and race—related to patients with varying stages of type 2 diabetes mellitus (T2DM), comparing the utilization of telemedicine (TM) with traditional healthcare face-to-face (F2F) appointments in an urban medically underserved population area (UMUPA). A logistic regression model, was used to analyze retrospective electronic patient health records (EHRs) from 1 January 2019 to 30 June 2021 of 265 patients with T2DM who had 3357 healthcare appointments. The overall percentage of healthcare provider appointments using TM was 46.7%, in comparison with 53.3% traditional F2F visits. Compared to patients with prediabetes, those with uncontrolled diabetes were more likely to utilize the TM mode of care rather than the traditional F2F mode (adjusted odds ratio (AoR), 1.33; confidence interval (CI), 1.07 to 1.64) after controlling for the other covariates in the model. Compared to patients in the age group 20–49 years, those in the age groups 50–64 years and ≥65 years had significantly lower odds (AoR, 0.78; CI, 0.65 to 0.94 and AoR, 0.71; CI, 0.58 to 0.88, respectively) of utilization of TM than the traditional F2F mode of care. White patients had significantly higher odds of using telemedicine rather than the traditional F2F mode (AoR, 1.25; CI, 1.07 to 1.47) when compared to the Black patients. Gender differences did not exist in the care utilization mode. As healthcare and public health continue to strive for health equity by eliminating health disparities within marginalized populations, it is essential that the mode of care for patients, such as those with T2DM, must evolve and adapt to the needs and resources of the patients. Multisectoral partners have the opportunity to employ a systems thinking approach to improve the technological elements related to the global health disparities crisis. An essential goal is to to create a user-friendly interface that prioritizes easy navigation, affordability, and accessiblity for populations in medically underserved regions to improve overall population health outcomes. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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20 pages, 2820 KiB

Open AccessArticle

A Machine Learning Pipeline for Cancer Detection on Microarray Data: The Role of Feature Discretization and Feature Selection

by Adara Nogueira, Artur Ferreira and Mário Figueiredo

BioMedInformatics 2023, 3(3), 585-604; https://doi.org/10.3390/biomedinformatics3030040 - 01 Aug 2023

Cited by 2 | Viewed by 1460

Abstract

Early disease detection using microarray data is vital for prompt and efficient treatment. However, the intricate nature of these data and the ongoing need for more precise interpretation techniques make it a persistently active research field. Numerous gene expression datasets are publicly available, [...] Read more.

Early disease detection using microarray data is vital for prompt and efficient treatment. However, the intricate nature of these data and the ongoing need for more precise interpretation techniques make it a persistently active research field. Numerous gene expression datasets are publicly available, containing microarray data that reflect the activation status of thousands of genes in patients who may have a specific disease. These datasets encompass a vast number of genes, resulting in high-dimensional feature vectors that present significant challenges for human analysis. Consequently, pinpointing the genes frequently associated with a particular disease becomes a crucial task. In this paper, we present a method capable of determining the frequency with which a gene (feature) is selected for the classification of a specific disease, by incorporating feature discretization and selection techniques into a machine learning pipeline. The experimental results demonstrate high accuracy and a low false negative rate, while significantly reducing the data’s dimensionality in the process. The resulting subsets of genes are manageable for clinical experts, enabling them to verify the presence of a given disease. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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Review

Jump to: Research

49 pages, 3730 KiB

Open AccessReview

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

by Hadrien T. Gayap and Moulay A. Akhloufi

BioMedInformatics 2024, 4(1), 236-284; https://doi.org/10.3390/biomedinformatics4010015 - 18 Jan 2024

Cited by 3 | Viewed by 2260

Abstract

Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes [...] Read more.

Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes the state-of-the-art in deep learning for lung cancer detection, highlighting key advances, limitations, and future directions. We prioritized studies utilizing major public datasets, such as LIDC, LUNA16, and JSRT, to provide a comprehensive overview of the field. We focus on deep learning architectures, including 2D and 3D convolutional neural networks (CNNs), dual-path networks, Natural Language Processing (NLP) and vision transformers (ViT). Across studies, deep learning models consistently outperformed traditional machine learning techniques in terms of accuracy, sensitivity, and specificity for lung cancer detection in CT scans. This is attributed to the ability of deep learning models to automatically learn discriminative features from medical images and model complex spatial relationships. However, several challenges remain to be addressed before deep learning models can be widely deployed in clinical practice. These include model dependence on training data, generalization across datasets, integration of clinical metadata, and model interpretability. Overall, deep learning demonstrates great potential for lung cancer detection and precision medicine. However, more research is required to rigorously validate models and address risks. This review provides key insights for both computer scientists and clinicians, summarizing progress and future directions for deep learning in medical image analysis. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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24 pages, 1137 KiB

Open AccessReview

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

by Anna Maria Pavone, Antonino Giulio Giannone, Daniela Cabibi, Simona D’Aprile, Simona Denaro, Giuseppe Salvaggio, Rosalba Parenti, Anthony Yezzi and Albert Comelli

BioMedInformatics 2024, 4(1), 173-196; https://doi.org/10.3390/biomedinformatics4010012 - 11 Jan 2024

Viewed by 1179

Abstract

In the era of digitalization, the biomedical sector has been affected by the spread of artificial intelligence. In recent years, the possibility of using deep and machine learning methods for clinical diagnostic and therapeutic interventions has been emerging as an essential resource for [...] Read more.

In the era of digitalization, the biomedical sector has been affected by the spread of artificial intelligence. In recent years, the possibility of using deep and machine learning methods for clinical diagnostic and therapeutic interventions has been emerging as an essential resource for biomedical imaging. Digital pathology represents innovation in a clinical world that looks for faster and better-performing diagnostic methods, without losing the accuracy of current human-guided analyses. Indeed, artificial intelligence has played a key role in a wide variety of applications that require the analysis of a massive amount of data, including segmentation processes in medical imaging. In this context, artificial intelligence enables the improvement of image segmentation methods, moving towards the development of fully automated systems of analysis able to support pathologists in decision-making procedures. The aim of this review is to aid biologists and clinicians in discovering the most common segmentation open-source tools, including ImageJ (v. 1.54), CellProfiler (v. 4.2.5), Ilastik (v. 1.3.3) and QuPath (v. 0.4.3), along with their customized implementations. Additionally, the tools’ role in the histological imaging field is explored further, suggesting potential application workflows. In conclusion, this review encompasses an examination of the most commonly segmented tissues and their analysis through open-source deep and machine learning tools. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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15 pages, 2649 KiB

Open AccessReview

Small Bowel Dose Constraints in Radiation Therapy—Where Omics-Driven Biomarkers and Bioinformatics Can Take Us in the Future

by Orly Yariv, Kevin Camphausen and Andra V. Krauze

BioMedInformatics 2024, 4(1), 158-172; https://doi.org/10.3390/biomedinformatics4010011 - 11 Jan 2024

Viewed by 1044

Abstract

Radiation-induced gastrointestinal (GI) dose constraints are still a matter of concern with the ongoing evolution of patient outcomes and treatment-related toxicity in the era of image-guided intensity-modulated radiation therapy (IMRT), stereotactic ablative radiotherapy (SABR), and novel systemic agents. Small bowel (SB) dose constraints [...] Read more.

Radiation-induced gastrointestinal (GI) dose constraints are still a matter of concern with the ongoing evolution of patient outcomes and treatment-related toxicity in the era of image-guided intensity-modulated radiation therapy (IMRT), stereotactic ablative radiotherapy (SABR), and novel systemic agents. Small bowel (SB) dose constraints in pelvic radiotherapy (RT) are a critical aspect of treatment planning, and prospective data to support them are scarce. Previous and current guidelines are based on retrospective data and experts’ opinions. Patient-related factors, including genetic, biological, and clinical features and systemic management, modulate toxicity. Omic and microbiome alterations between patients receiving RT to the SB may aid in the identification of patients at risk and real-time identification of acute and late toxicity. Actionable biomarkers may represent a pragmatic approach to translating findings into personalized treatment with biologically optimized dose escalation, given the mitigation of the understood risk. Biomarkers grounded in the genome, transcriptome, proteome, and microbiome should undergo analysis in trials that employ, R.T. Bioinformatic templates will be needed to help advance data collection, aggregation, and analysis, and eventually, decision making with respect to dose constraints in the modern RT era. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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33 pages, 1697 KiB

Open AccessReview

Genomics for Emerging Pathogen Identification and Monitoring: Prospects and Obstacles

by Vishakha Vashisht, Ashutosh Vashisht, Ashis K. Mondal, Jaspreet Farmaha, Ahmet Alptekin, Harmanpreet Singh, Pankaj Ahluwalia, Anaka Srinivas and Ravindra Kolhe

BioMedInformatics 2023, 3(4), 1145-1177; https://doi.org/10.3390/biomedinformatics3040069 - 07 Dec 2023

Cited by 2 | Viewed by 2663

Abstract

Emerging infectious diseases (EIDs) pose an increasingly significant global burden, driven by urbanization, population explosion, global travel, changes in human behavior, and inadequate public health systems. The recent SARS-CoV-2 pandemic highlights the urgent need for innovative and robust technologies to effectively monitor newly [...] Read more.

Emerging infectious diseases (EIDs) pose an increasingly significant global burden, driven by urbanization, population explosion, global travel, changes in human behavior, and inadequate public health systems. The recent SARS-CoV-2 pandemic highlights the urgent need for innovative and robust technologies to effectively monitor newly emerging pathogens. Rapid identification, epidemiological surveillance, and transmission mitigation are crucial challenges for ensuring public health safety. Genomics has emerged as a pivotal tool in public health during pandemics, enabling the diagnosis, management, and prediction of infections, as well as the analysis and identification of cross-species interactions and the categorization of infectious agents. Recent advancements in high-throughput DNA sequencing tools have facilitated rapid and precise identification and characterization of emerging pathogens. This review article provides insights into the latest advances in various genomic techniques for pathogen detection and tracking and their applications in global outbreak surveillance. We assess methods that leverage pathogen sequences and explore the role of genomic analysis in understanding the epidemiology of newly emerged infectious diseases. Additionally, we address technical challenges and limitations, ethical and legal considerations, and highlight opportunities for integrating genomics with other surveillance approaches. By delving into the prospects and obstacles of genomics, we can gain valuable insights into its role in mitigating the threats posed by emerging pathogens and improving global preparedness in the face of future outbreaks. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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11 pages, 636 KiB

Open AccessReview

Deciphering the Mosaic of Therapeutic Potential: A Scoping Review of Neural Network Applications in Psychotherapy Enhancements

by Alexandre Hudon, Maxine Aird and Noémie La Haye-Caty

BioMedInformatics 2023, 3(4), 1101-1111; https://doi.org/10.3390/biomedinformatics3040066 - 01 Dec 2023

Cited by 1 | Viewed by 1387

Abstract

Background: Psychotherapy is a component of the therapeutic options accessible in mental health. Along with psychotherapy techniques and indications, there is a body of studies on what are known as psychotherapy’s common factors. However, up to 40% of patients do not respond to [...] Read more.

Background: Psychotherapy is a component of the therapeutic options accessible in mental health. Along with psychotherapy techniques and indications, there is a body of studies on what are known as psychotherapy’s common factors. However, up to 40% of patients do not respond to therapy. Artificial intelligence approaches are hoped to enhance this and with the growing body of evidence of the use of neural networks (NNs) in other areas of medicine, this domain is lacking in the field of psychotherapy. This study aims to identify the different uses of NNs in the field of psychotherapy. Methods: A scoping review was conducted in the electronic databases EMBASE, MEDLINE, APA, and CINAHL. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement influenced this study’s design. Studies were included if they applied a neural network algorithm in the context of a psychotherapeutic approach. Results: A total of 157 studies were screened for eligibility, of which 32 were fully assessed. Finally, eight articles were analyzed, and three uses were identified: predicting the therapeutic outcomes, content analysis, and automated categorization of psychotherapeutic interactions. Conclusions: Uses of NNs were identified with limited evidence of their effects. The potential implications of these uses could assist the therapist in providing a more personalized therapeutic approach to their patients. Given the paucity of literature, this study provides a path for future research to better understand the efficacy of such uses. Full article

(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)

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