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Artificial Intelligence in the Detection of Diseases

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A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 36062

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Special Issue Editor

Dr. I-Shiang Tzeng

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Guest Editor

Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
Interests: genetic risk score; differentially expressed genes; data mining and analysis; machine learning; artificial intelligence; epidemiology; biostatistics; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of artificial intelligence refers to the development and application of classifiers in medicine, epidemiology and other science fields. In recent years, artificial intelligence has played an important role in disease detection. Classifiers constructed by artificial intelligence can recognize undiagnosed patients. Therefore, this Special Issue of Biomedicines entitled “Artificial Intelligence in the Detection of Diseases” invites scholars to submit their research on artificial intelligence methods in disease detection and thus aims to present the latest advances in this field.

Dr. I-Shiang Tzeng
Guest Editor

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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. Biomedicines 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 2600 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

internal and surgical diseases
artificial intelligence
machine learning
data mining
diagnosis
prognosis

Published Papers (10 papers)

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Research

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18 pages, 2124 KiB

Open AccessArticle

Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa-SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients

by Iván Salgado, Ernesto Prado Montes de Oca, Isaac Chairez, Luis Figueroa-Yáñez, Alejandro Pereira-Santana, Andrés Rivera Chávez, Jesús Bernardino Velázquez-Fernandez, Teresa Alvarado Parra and Adriana Vallejo

Biomedicines 2024, 12(2), 395; https://doi.org/10.3390/biomedicines12020395 - 8 Feb 2024

Viewed by 1389

Abstract

The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number

> 4

times in the dead compared with the alive group) were EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3−3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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19 pages, 5623 KiB

Open AccessArticle

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

by Muhammad Shahab, Guojun Zheng, Abbas Khan, Dongqing Wei and Alexander S. Novikov

Biomedicines 2023, 11(8), 2251; https://doi.org/10.3390/biomedicines11082251 - 11 Aug 2023

Cited by 10 | Viewed by 2902

Abstract

Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2’s active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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15 pages, 7604 KiB

Open AccessArticle

MSF-Net: A Lightweight Multi-Scale Feature Fusion Network for Skin Lesion Segmentation

by Dangguo Shao, Lifan Ren and Lei Ma

Biomedicines 2023, 11(6), 1733; https://doi.org/10.3390/biomedicines11061733 - 16 Jun 2023

Cited by 3 | Viewed by 1206

Abstract

Segmentation of skin lesion images facilitates the early diagnosis of melanoma. However, this remains a challenging task due to the diversity of target scales, irregular segmentation shapes, low contrast, and blurred boundaries of dermatological graphics. This paper proposes a multi-scale feature fusion network (MSF-Net) based on comprehensive attention convolutional neural network (CA-Net). We introduce the spatial attention mechanism in the convolution block through the residual connection to focus on the key regions. Meanwhile, Multi-scale Dilated Convolution Modules (MDC) and Multi-scale Feature Fusion Modules (MFF) are introduced to extract context information across scales and adaptively adjust the receptive field size of the feature map. We conducted many experiments on the public data set ISIC2018 to verify the validity of MSF-Net. The ablation experiment demonstrated the effectiveness of our three modules. The comparison experiment with the existing advanced network confirms that MSF-Net can achieve better segmentation under fewer parameters. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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12 pages, 3012 KiB

Open AccessArticle

Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

by Taukir Alam, Wei-Chung Shia, Fang-Rong Hsu and Taimoor Hassan

Biomedicines 2023, 11(6), 1536; https://doi.org/10.3390/biomedicines11061536 - 25 May 2023

Cited by 5 | Viewed by 3099

Abstract

We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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12 pages, 1131 KiB

Open AccessArticle

Classification of Highly Divergent Viruses from DNA/RNA Sequence Using Transformer-Based Models

by Tariq Sadad, Raja Atif Aurangzeb, Mejdl Safran, Imran, Sultan Alfarhood and Jungsuk Kim

Biomedicines 2023, 11(5), 1323; https://doi.org/10.3390/biomedicines11051323 - 28 Apr 2023

Cited by 1 | Viewed by 2313

Abstract

Viruses infect millions of people worldwide each year, and some can lead to cancer or increase the risk of cancer. As viruses have highly mutable genomes, new viruses may emerge in the future, such as COVID-19 and influenza. Traditional virology relies on predefined rules to identify viruses, but new viruses may be completely or partially divergent from the reference genome, rendering statistical methods and similarity calculations insufficient for all genome sequences. Identifying DNA/RNA-based viral sequences is a crucial step in differentiating different types of lethal pathogens, including their variants and strains. While various tools in bioinformatics can align them, expert biologists are required to interpret the results. Computational virology is a scientific field that studies viruses, their origins, and drug discovery, where machine learning plays a crucial role in extracting domain- and task-specific features to tackle this challenge. This paper proposes a genome analysis system that uses advanced deep learning to identify dozens of viruses. The system uses nucleotide sequences from the NCBI GenBank database and a BERT tokenizer to extract features from the sequences by breaking them down into tokens. We also generated synthetic data for viruses with small sample sizes. The proposed system has two components: a scratch BERT architecture specifically designed for DNA analysis, which is used to learn the next codons unsupervised, and a classifier that identifies important features and understands the relationship between genotype and phenotype. Our system achieved an accuracy of 97.69% in identifying viral sequences. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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11 pages, 2838 KiB

Open AccessArticle

Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays

by Yu-Jiun Fan, I-Shiang Tzeng, Yao-Sian Huang, Yuan-Yu Hsu, Bo-Chun Wei, Shuo-Ting Hung and Yeung-Leung Cheng

Biomedicines 2023, 11(3), 760; https://doi.org/10.3390/biomedicines11030760 - 2 Mar 2023

Cited by 2 | Viewed by 3244

Abstract

Pectus excavatum (PE), a chest-wall deformity that can compromise cardiopulmonary function, cannot be detected by a radiologist through frontal chest radiography without a lateral view or chest computed tomography. This study aims to train a convolutional neural network (CNN), a deep learning architecture with powerful image processing ability, for PE screening through frontal chest radiography, which is the most common imaging test in current hospital practice. Posteroanterior-view chest images of PE and normal patients were collected from our hospital to build the database. Among them, 80% were used as the training set used to train the established CNN algorithm, Xception, whereas the remaining 20% were a test set for model performance evaluation. The performance of our diagnostic artificial intelligence model ranged between 0.976–1 under the receiver operating characteristic curve. The test accuracy of the model reached 0.989, and the sensitivity and specificity were 96.66 and 96.64, respectively. Our study is the first to prove that a CNN can be trained as a diagnostic tool for PE using frontal chest X-rays, which is not possible by the human eye. It offers a convenient way to screen potential candidates for the surgical repair of PE, primarily using available image examinations. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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15 pages, 767 KiB

Open AccessArticle

A Deep Convolutional Neural Network for the Early Detection of Heart Disease

by Sadia Arooj, Saif ur Rehman, Azhar Imran, Abdullah Almuhaimeed, A. Khuzaim Alzahrani and Abdulkareem Alzahrani

Biomedicines 2022, 10(11), 2796; https://doi.org/10.3390/biomedicines10112796 - 3 Nov 2022

Cited by 18 | Viewed by 6365

Abstract

Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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Review

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23 pages, 1807 KiB

Open AccessReview

Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis

by Elena Ivanova, Alexey Fayzullin, Victor Grinin, Dmitry Ermilov, Alexander Arutyunyan, Peter Timashev and Anatoly Shekhter

Biomedicines 2023, 11(11), 2875; https://doi.org/10.3390/biomedicines11112875 - 24 Oct 2023

Cited by 3 | Viewed by 1434

Abstract

Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital pathology tools emerged as a promising solution to enhance the diagnosis and management of renal cancer. This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. Through advanced image analysis algorithms, artificial intelligence (AI) technologies facilitate quantification of cellular and molecular markers, leading to improved accuracy and reproducibility in renal cancer diagnosis. Digital pathology platforms empower remote collaboration between pathologists and help with the creation of comprehensive databases for further research and machine learning applications. The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. In this article, we explored the digital pathology instruments available for clear cell, papillary and chromophobe renal cancers from pathologist and data analyst perspectives. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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19 pages, 750 KiB

Open AccessReview

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

by Giuseppe Miceli, Maria Grazia Basso, Giuliana Rizzo, Chiara Pintus, Elena Cocciola, Andrea Roberta Pennacchio and Antonino Tuttolomondo

Biomedicines 2023, 11(4), 1138; https://doi.org/10.3390/biomedicines11041138 - 10 Apr 2023

Cited by 9 | Viewed by 2969

Abstract

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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23 pages, 1085 KiB

Open AccessReview

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

by Anna Visibelli, Bianca Roncaglia, Ottavia Spiga and Annalisa Santucci

Biomedicines 2023, 11(3), 887; https://doi.org/10.3390/biomedicines11030887 - 13 Mar 2023

Cited by 14 | Viewed by 9439

Abstract

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1–9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)

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