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Diagnostics
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Artificial Neural Networks in Medical Diagnosis
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Artificial Neural Networks in Medical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 49702

Special Issue Editor

Prof. Dr. Ming-Huwi Horng

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Pingtung University, 51 Min Sheng E. Road, Pingtung 900, Taiwan
Interests: artificial neural network; machine learning; medical image analysis; deep learning; pattern recognition

Special Issue Information

Dear Colleagues,

Medical diagnosis provides explanations of different patient’s health problems based on the applications of artificial neural networks. To date, several technologies have been proposed to analyze medical data sets and different modality images to make diagnosis decisions. For example, artificial neural networks promise novel combinations of treatment protocols ensuring the most relevant and cost-effective and precisely targeting therapies in clinical medicine. In medical imaging, neural networks have the potential to lead to safer, faster, and more affordable diagnosis compared to using the existing imaging modalities ranging from optical analysis to radiographic imaging and nuclear medicine. This Special Issue is intended to lay the foundation of clinical artificial neural network applications focusing on case studies in medical data and image analysis, discuss several applications of neural network diagnostics with emphasis on personalized medicine, and offer an overview of frameworks for the broader integration of different algorithms in clinical practice.

Potential topics include but are not limited to the following:

  • Algorithms, methods, frameworks, and best practices for data and medical image analysis based on methods of artificial neural networks such as deep learning, machine learning, and reinforcement learning;
  • Methods for identifying interactions and integrating different data modalities using neural networks;
  • Methods for integrating non-imaging data, such as next-generation gene sequencing data.

Prof. Dr. Ming-Huwi Horng
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. Diagnostics is an international peer-reviewed open access semimonthly 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

  • medical diagnosis
  • neural network
  • machine learning
  • deep learning
  • reinforcement learning
  • big data analytics
  • medical images analysis
  • gene sequencing data analysis

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Published Papers (6 papers)

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Research

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22 pages, 2134 KiB  
Article
A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection
by Lei Zhang, Linjie Wu, Liangzhuang Wei, Haitao Wu and Yandan Lin
Diagnostics 2023, 13(6), 1151; https://doi.org/10.3390/diagnostics13061151 - 17 Mar 2023
Cited by 2 | Viewed by 1835
Abstract
Narrow band imaging is an established non-invasive tool used for the early detection of laryngeal cancer in surveillance examinations. Most images produced from the examination are useless, such as blurred, specular reflection, and underexposed. Removing the uninformative frames is vital to improve detection [...] Read more.
Narrow band imaging is an established non-invasive tool used for the early detection of laryngeal cancer in surveillance examinations. Most images produced from the examination are useless, such as blurred, specular reflection, and underexposed. Removing the uninformative frames is vital to improve detection accuracy and speed up computer-aided diagnosis. It often takes a lot of time for the physician to manually inspect the informative frames. This issue is commonly addressed by a classifier with task-specific categories of the uninformative frames. However, the definition of the uninformative categories is ambiguous, and tedious labeling still cannot be avoided. Here, we show that a novel unsupervised scheme is comparable to the current benchmarks on the dataset of NBI-InfFrames. We extract feature embedding using a vanilla neural network (VGG16) and introduce a new dimensionality reduction method called UMAP that distinguishes the feature embedding in the lower-dimensional space. Along with the proposed automatic cluster labeling algorithm and cost function in Bayesian optimization, the proposed method coupled with UMAP achieves state-of-the-art performance. It outperforms the baseline by 12% absolute. The overall median recall of the proposed method is currently the highest, 96%. Our results demonstrate the effectiveness of the proposed scheme and the robustness of detecting the informative frames. It also suggests the patterns embedded in the data help develop flexible algorithms that do not require manual labeling. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Medical Diagnosis)
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13 pages, 2057 KiB  
Article
An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis
by Anri Patron, Leevi Annala, Olli Lainiala, Juha Paloneva and Sami Äyrämö
Diagnostics 2022, 12(11), 2603; https://doi.org/10.3390/diagnostics12112603 - 27 Oct 2022
Cited by 4 | Viewed by 30013
Abstract
Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease’s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of [...] Read more.
Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease’s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis. Previous research has demonstrated an association between knee osteoarthritis and tibial spiking, but the connection to the early-stage disease has not been investigated. We study tibial spiking as a feature of early knee osteoarthritis. Additionally, we develop a deep learning based model for detecting tibial spiking from plain radiographs. We collected and graded 913 knee radiographs for tibial spiking. We conducted two experiments: experiments A and B. In experiment A, we compared the subjects with and without tibial spiking using Mann-Whitney U-test. Experiment B consisted of developing and validating an interpretative deep learning based method for predicting tibial spiking. The subjects with tibial spiking had more severe Kellgren-Lawrence grade, medial joint space narrowing, and osteophyte score in the lateral tibial compartment. The developed method achieved an accuracy of 0.869. We find tibial spiking a promising feature in knee osteoarthritis diagnosis. Furthermore, the detection can be automatized. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Medical Diagnosis)
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12 pages, 2661 KiB  
Article
Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism on 18F-FDG PET/CT and Automated Classifiers
by Susanna Nuvoli, Francesco Bianconi, Maria Rondini, Achille Lazzarato, Andrea Marongiu, Mario Luca Fravolini, Silvia Cascianelli, Serena Amici, Luca Filippi, Angela Spanu and Barbara Palumbo
Diagnostics 2022, 12(10), 2425; https://doi.org/10.3390/diagnostics12102425 - 7 Oct 2022
Cited by 2 | Viewed by 2205
Abstract
Purpose: We evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain 18F-FDG PET/CT to improve the differential diagnosis between Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). Procedures: We retrospectively analyzed a total of 150 [...] Read more.
Purpose: We evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain 18F-FDG PET/CT to improve the differential diagnosis between Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). Procedures: We retrospectively analyzed a total of 150 consecutive patients who underwent diagnostic evaluation for suspected AD (n = 67) or MCI (n = 83). All patients received brain 18F-FDG PET/CT according to the international guidelines, and images were analyzed both Qualitatively (QL) and Quantitatively (QN), the latter by a fully automated post-processing software that produced a z score metabolic map of 25 anatomically different cortical regions. A subset of n = 122 cases with a confirmed diagnosis of AD (n = 53) or MDI (n = 69) by 18–24-month clinical follow-up was finally included in the study. Univariate analysis and three automated classification models (classification tree –ClT-, ridge classifier –RC- and linear Support Vector Machine –lSVM-) were considered to estimate the ability of the z scores to discriminate between AD and MCI cases in. Results: The univariate analysis returned 14 areas where the z scores were significantly different between AD and MCI groups, and the classification accuracy ranged between 74.59% and 76.23%, with ClT and RC providing the best results. The best classification strategy consisted of one single split with a cut-off value of ≈ −2.0 on the z score from temporal lateral left area: cases below this threshold were classified as AD and those above the threshold as MCI. Conclusions: Our findings confirm the usefulness of brain 18F-FDG PET/CT QL and QN analyses in differentiating AD from MCI. Moreover, the combined use of automated classifications models can improve the diagnostic process since its use allows identification of a specific hypometabolic area involved in AD cases in respect to MCI. This data improves the traditional 18F-FDG PET/CT image interpretation and the diagnostic assessment of cognitive disorders. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Medical Diagnosis)
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24 pages, 2351 KiB  
Article
Detecting Endotracheal Tube and Carina on Portable Supine Chest Radiographs Using One-Stage Detector with a Coarse-to-Fine Attention
by Liang-Kai Mao, Min-Hsin Huang, Chao-Han Lai, Yung-Nien Sun and Chi-Yeh Chen
Diagnostics 2022, 12(8), 1913; https://doi.org/10.3390/diagnostics12081913 - 7 Aug 2022
Cited by 1 | Viewed by 2455
Abstract
In intensive care units (ICUs), after endotracheal intubation, the position of the endotracheal tube (ETT) should be checked to avoid complications. The malposition can be detected by the distance between the ETT tip and the Carina (ETT–Carina distance). However, it struggles with a [...] Read more.
In intensive care units (ICUs), after endotracheal intubation, the position of the endotracheal tube (ETT) should be checked to avoid complications. The malposition can be detected by the distance between the ETT tip and the Carina (ETT–Carina distance). However, it struggles with a limited performance for two major problems, i.e., occlusion by external machine, and the posture and machine of taking chest radiographs. While previous studies addressed these problems, they always suffered from the requirements of manual intervention. Therefore, the purpose of this paper is to locate the ETT tip and the Carina more accurately for detecting the malposition without manual intervention. The proposed architecture is composed of FCOS: Fully Convolutional One-Stage Object Detection, an attention mechanism named Coarse-to-Fine Attention (CTFA), and a segmentation branch. Moreover, a post-process algorithm is adopted to select the final location of the ETT tip and the Carina. Three metrics were used to evaluate the performance of the proposed method. With the dataset provided by National Cheng Kung University Hospital, the accuracy of the malposition detected by the proposed method achieves 88.82% and the ETT–Carina distance errors are less than 5.333±6.240 mm. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Medical Diagnosis)
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26 pages, 10492 KiB  
Article
A Comprehensive Performance Analysis of Transfer Learning Optimization in Visual Field Defect Classification
by Masyitah Abu, Nik Adilah Hanin Zahri, Amiza Amir, Muhammad Izham Ismail, Azhany Yaakub, Said Amirul Anwar and Muhammad Imran Ahmad
Diagnostics 2022, 12(5), 1258; https://doi.org/10.3390/diagnostics12051258 - 18 May 2022
Cited by 4 | Viewed by 6822
Abstract
Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced [...] Read more.
Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced a comprehensive comparative analysis to compare the performance of different CNN models before and after hyperparameter tuning and fine-tuning. Using 32 batch sizes, 50 epochs, and ADAM as the optimizer to optimize weight, bias, and learning rate, VGG-16 obtained the highest accuracy of 97.63 percent, according to experimental findings. Subsequently, Bayesian optimization was utilized to execute automated hyperparameter tuning and automated fine-tuning layers of the pre-trained models to determine the optimal hyperparameter and fine-tuning layer for classifying many VF defect with the highest accuracy. We found that the combination of different hyperparameters and fine-tuning of the pre-trained models significantly impact the performance of deep learning models for this classification task. In addition, we also discovered that the automated selection of optimal hyperparameters and fine-tuning by Bayesian has significantly enhanced the performance of the pre-trained models. The results observed the best performance for the DenseNet-121 model with a validation accuracy of 98.46% and a test accuracy of 99.57% for the tested datasets. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Medical Diagnosis)
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Review

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21 pages, 5715 KiB  
Review
Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey
by Sheng-Yao Huang, Wen-Lin Hsu, Ren-Jun Hsu and Dai-Wei Liu
Diagnostics 2022, 12(11), 2765; https://doi.org/10.3390/diagnostics12112765 - 11 Nov 2022
Cited by 24 | Viewed by 4024
Abstract
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies [...] Read more.
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for future development, including model development, data augmentation processing, and dataset creation. The strengths and deficiencies of studies on models and data augmentation, as well as their application to medical image segmentation, were analyzed. Fully convolutional network developments have led to the creation of the U-Net and its derivatives. Another noteworthy image segmentation model is DeepLab. Regarding data augmentation, due to the low data volume of medical images, most studies focus on means to increase the wealth of medical image data. Generative adversarial networks (GAN) increase data volume via deep learning. Despite the increasing types of medical image datasets, there is still a deficiency of datasets on specific problems, which should be improved moving forward. Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Medical Diagnosis)
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