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Machine Learning and Medicine: The Interface of Medicine, Engineering and...
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Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 12433

Special Issue Editor

Prof. Dr. Simon W. Rabkin

E-Mail Website
Guest Editor
Department of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: heart failure; hypertension; lipids; aortic aneurysm; cardiovascular disease and its therapy; translational medicine; AI in cardiovascular diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has increasingly been applied in medical research. The field of medicine is data-rich, and machine learning, which utilizes computer systems to make decisions based on data, can provide invaluable insights into these data. There are many ways in which machine learning can aid in the assessment, diagnosis, and treatment of diseases, from analyzing medical records or diagnostic testing to predicting patient outcomes.

This Special Issue titled "Machine Learning and Medicine: the interface of Medicine, Engineering and Artificial Intelligence" explores the potential applications and challenges of machine learning in the field of medicine. It highlights the growing interface between computer science and medicine, and how the integration of these fields can provide innovative solutions to healthcare challenges.

The Special Issue covers a diverse set of topics, including the use of machine learning in disease classification, diagnosis, treatment, and patient monitoring. It also delves into issues related to the ethical implications of using machine learning in healthcare, such as data privacy and security.

Overall, the Special Issue highlights the enormous potential of machine learning techniques in transforming healthcare, while also acknowledging the limitations and challenges associated with their implementation. It provides an important platform for researchers and practitioners to share their expertise on this rapidly evolving field and encourage interdisciplinary collaboration to shape the future of medicine.

Prof. Dr. Simon Rabkin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical informatics
  • healthcare analytics
  • predictive modeling
  • digital health
  • precision medicine
  • machine learning
  • artificial intelligence

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Published Papers (7 papers)

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Research

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15 pages, 5380 KiB  
Article
Lightweight Super-Resolution Techniques in Medical Imaging: Bridging Quality and Computational Efficiency
by Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Zaripova Dilnoza, Kudratjon Zohirov, Rashid Nasimov, Sabina Umirzakova and Young-Im Cho
Bioengineering 2024, 11(12), 1179; https://doi.org/10.3390/bioengineering11121179 - 21 Nov 2024
Viewed by 247
Abstract
Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) [...] Read more.
Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images. In this paper, we propose an enhanced Residual Feature Learning Network (RFLN) tailored specifically for medical imaging. Our contributions include replacing the residual local feature blocks with standard residual blocks, increasing the model depth for improved feature extraction, and incorporating enhanced spatial attention (ESA) mechanisms to refine the feature selection. Extensive experiments on medical imaging datasets demonstrate that the proposed model achieves superior performance in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality compared to existing state-of-the-art models. The enhanced RFLN not only effectively mitigates noise but also preserves critical anatomical details, making it a promising solution for high-precision medical imaging applications. Full article
(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)
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15 pages, 1943 KiB  
Article
Improving Radiology Report Generation Quality and Diversity through Reinforcement Learning and Text Augmentation
by Daniel Parres, Alberto Albiol and Roberto Paredes
Bioengineering 2024, 11(4), 351; https://doi.org/10.3390/bioengineering11040351 - 3 Apr 2024
Viewed by 1718
Abstract
Deep learning is revolutionizing radiology report generation (RRG) with the adoption of vision encoder–decoder (VED) frameworks, which transform radiographs into detailed medical reports. Traditional methods, however, often generate reports of limited diversity and struggle with generalization. Our research introduces reinforcement learning and text [...] Read more.
Deep learning is revolutionizing radiology report generation (RRG) with the adoption of vision encoder–decoder (VED) frameworks, which transform radiographs into detailed medical reports. Traditional methods, however, often generate reports of limited diversity and struggle with generalization. Our research introduces reinforcement learning and text augmentation to tackle these issues, significantly improving report quality and variability. By employing RadGraph as a reward metric and innovating in text augmentation, we surpass existing benchmarks like BLEU4, ROUGE-L, F1CheXbert, and RadGraph, setting new standards for report accuracy and diversity on MIMIC-CXR and Open-i datasets. Our VED model achieves F1-scores of 66.2 for CheXbert and 37.8 for RadGraph on the MIMIC-CXR dataset, and 54.7 and 45.6, respectively, on Open-i. These outcomes represent a significant breakthrough in the RRG field. The findings and implementation of the proposed approach, aimed at enhancing diagnostic precision and radiological interpretations in clinical settings, are publicly available on GitHub to encourage further advancements in the field. Full article
(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)
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9 pages, 571 KiB  
Article
Exploring Diagnostic Precision and Triage Proficiency: A Comparative Study of GPT-4 and Bard in Addressing Common Ophthalmic Complaints
by Roya Zandi, Joseph D. Fahey, Michael Drakopoulos, John M. Bryan, Siyuan Dong, Paul J. Bryar, Ann E. Bidwell, R. Chris Bowen, Jeremy A. Lavine and Rukhsana G. Mirza
Bioengineering 2024, 11(2), 120; https://doi.org/10.3390/bioengineering11020120 - 26 Jan 2024
Cited by 11 | Viewed by 2062
Abstract
In the modern era, patients often resort to the internet for answers to their health-related concerns, and clinics face challenges to providing timely response to patient concerns. This has led to a need to investigate the capabilities of AI chatbots for ophthalmic diagnosis [...] Read more.
In the modern era, patients often resort to the internet for answers to their health-related concerns, and clinics face challenges to providing timely response to patient concerns. This has led to a need to investigate the capabilities of AI chatbots for ophthalmic diagnosis and triage. In this in silico study, 80 simulated patient complaints in ophthalmology with varying urgency levels and clinical descriptors were entered into both ChatGPT and Bard in a systematic 3-step submission process asking chatbots to triage, diagnose, and evaluate urgency. Three ophthalmologists graded chatbot responses. Chatbots were significantly better at ophthalmic triage than diagnosis (90.0% appropriate triage vs. 48.8% correct leading diagnosis; p < 0.001), and GPT-4 performed better than Bard for appropriate triage recommendations (96.3% vs. 83.8%; p = 0.008), grader satisfaction for patient use (81.3% vs. 55.0%; p < 0.001), and lower potential harm rates (6.3% vs. 20.0%; p = 0.010). More descriptors improved the accuracy of diagnosis for both GPT-4 and Bard. These results indicate that chatbots may not need to recognize the correct diagnosis to provide appropriate ophthalmic triage, and there is a potential utility of these tools in aiding patients or triage staff; however, they are not a replacement for professional ophthalmic evaluation or advice. Full article
(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)
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16 pages, 4681 KiB  
Article
Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares
by Iciar Usategui, Yoel Arroyo, Ana María Torres, Julia Barbado and Jorge Mateo
Bioengineering 2024, 11(1), 90; https://doi.org/10.3390/bioengineering11010090 - 17 Jan 2024
Cited by 5 | Viewed by 2028
Abstract
Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving [...] Read more.
Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems. Full article
(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)
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26 pages, 3577 KiB  
Article
Optimizing Skin Cancer Survival Prediction with Ensemble Techniques
by Erum Yousef Abbasi, Zhongliang Deng, Arif Hussain Magsi, Qasim Ali, Kamlesh Kumar and Asma Zubedi
Bioengineering 2024, 11(1), 43; https://doi.org/10.3390/bioengineering11010043 - 31 Dec 2023
Cited by 1 | Viewed by 1915
Abstract
The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This [...] Read more.
The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan–Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy. Full article
(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)
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Review

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16 pages, 3090 KiB  
Review
Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review
by Simon W Rabkin
Bioengineering 2024, 11(5), 489; https://doi.org/10.3390/bioengineering11050489 - 15 May 2024
Cited by 2 | Viewed by 1457
Abstract
Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical [...] Read more.
Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators “left ventricular hypertrophy, electrocardiogram” AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database. Full article
(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)
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17 pages, 1824 KiB  
Review
Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence
by Carlo Metta, Andrea Beretta, Roberto Pellungrini, Salvatore Rinzivillo and Fosca Giannotti
Bioengineering 2024, 11(4), 369; https://doi.org/10.3390/bioengineering11040369 - 12 Apr 2024
Cited by 4 | Viewed by 1743
Abstract
This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, [...] Read more.
This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians’ and patients’ understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards. Full article
(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)
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