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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 11471

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Special Issue Editors

Dr. Sadiq Hussain

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Guest Editor

System Administrator, Dibrugarh University, Dibrugarh, India
Interests: deep learning; convolutional neural networks; medical diagnosis; medical image processing; explainable AI; healthcare

Dr. Roohallah Alizadehsani

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Guest Editor

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
Interests: data mining; machine learning; bioinformatics; computational biology; data sciences
Special Issues, Collections and Topics in MDPI journals

Dr. Mohamad Roshanzamir

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Guest Editor

Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa 74617-81189, Iran
Interests: deep learning; convolutional neural networks; medical diagnosis; medical image processing; explainable AI; healthcare

Special Issue Information

Dear Colleagues,

In recent decades, deep learning technology has been used widely for medical image analysis as it shows high performance in different applications such as image diagnosis. Although deep learning networks can be used in different scientific fields, they have quickly been used extensively in medical image processing. In this field, high-quality results of deep learning algorithms could speed up the decision-making process in clinical environments. Using deep learning methods, the researchers can develop medical image processing applications such as the segmentation of medical images and their use in the automated detection of different pathologies. These algorithms can also be used for efficient data processing, analysis, the modeling of the generated data which are crucially important for clinical applications and understanding the underlying biological process.The purpose of this Special Issue, entitled “Deep Learning for Medical Imaging Diagnosis”, is to report and present new algorithms, techniques, and applications of deep learning in medical image analysis. We invite researchers to submit articles related, but not limited to, the following topics:

Deep learning for different medical image processing;
Graph CNN models for medical image processing;
MRI image processing using deep neural networks;
Deep learning models used in biomedical applications;
Deep learning for other medical imaging applications;
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.

Dr. Sadiq Hussain
Dr. Roohallah Alizadehsani
Dr. Mohamad Roshanzamir
Guest Editors

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.

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Published Papers (7 papers)

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12 pages, 1794 KiB

Open AccessArticle

Accuracy of Treatment Recommendations by Pragmatic Evidence Search and Artificial Intelligence: An Exploratory Study

by Zunaira Baig, Daniel Lawrence, Mahen Ganhewa and Nicola Cirillo

Diagnostics 2024, 14(5), 527; https://doi.org/10.3390/diagnostics14050527 - 1 Mar 2024

Cited by 2 | Viewed by 1490

Abstract

There is extensive literature emerging in the field of dentistry with the aim to optimize clinical practice. Evidence-based guidelines (EBGs) are designed to collate diagnostic criteria and clinical treatment for a range of conditions based on high-quality evidence. Recently, advancements in Artificial Intelligence (AI) have instigated further queries into its applicability and integration into dentistry. Hence, the aim of this study was to develop a model that can be used to assess the accuracy of treatment recommendations for dental conditions generated by individual clinicians and the outcomes of AI outputs. For this pilot study, a Delphi panel of six experts led by CoTreat AI provided the definition and developed evidence-based recommendations for subgingival and supragingival calculus. For the rapid review—a pragmatic approach that aims to rapidly assess the evidence base using a systematic methodology—the Ovid Medline database was searched for subgingival and supragingival calculus. Studies were selected and reported based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), and this study complied with the minimum requirements for completing a restricted systematic review. Treatment recommendations were also searched for these same conditions in ChatGPT (version 3.5 and 4) and Bard (now Gemini). Adherence to the recommendations of the standard was assessed using qualitative content analysis and agreement scores for interrater reliability. Treatment recommendations by AI programs generally aligned with the current literature, with an agreement of up to 75%, although data sources were not provided by these tools, except for Bard. The clinician’s rapid review results suggested several procedures that may increase the likelihood of overtreatment, as did GPT4. In terms of overall accuracy, GPT4 outperformed all other tools, including rapid review (Cohen’s kappa 0.42 vs. 0.28). In summary, this study provides preliminary observations for the suitability of different evidence-generating methods to inform clinical dental practice. Full article

(This article belongs to the Special Issue Deep Learning for Medical Imaging Diagnosis)

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10 pages, 927 KiB

Open AccessArticle

A Quantitative Evaluation of the Effectiveness of the Metal Artifact Reduction Algorithm in Cone Beam Computed Tomographic Images with Stainless Steel Orthodontic Brackets and Arch Wires: An Ex Vivo Study

by Mojgan Shavakhi, Parisa Soltani, Golnaz Aghababaee, Romeo Patini, Niccolò Giuseppe Armogida, Gianrico Spagnuolo and Alessandra Valletta

Diagnostics 2024, 14(2), 159; https://doi.org/10.3390/diagnostics14020159 - 10 Jan 2024

Viewed by 1333

Abstract

The presence of high-density and high-atomic number materials results in the generation of artifacts in cone beam computed tomographic (CBCT) images. To minimize artifacts in CBCT images, the metal artifact reduction (MAR) tool was developed. This study aims to quantitatively evaluate the effectiveness of the MAR algorithm in CBCT images of teeth with stainless steel orthodontic brackets with or without arch wires in buccal and lingual positions obtained using the Galileos Sirona CBCT scanner. In this in vitro study, 20 stainless steel brackets were attached to the maxillary dentition from the right second premolar to the left second premolar teeth of a human skull. In the first group, 10 brackets were bonded to the buccal surface, and in the second group, 10 brackets were bonded to the palatal surface of these teeth. CBCT scans were obtained for each group with or without orthodontic stainless steel wires using a Galileos Sirona CBCT scanner with exposure parameters of 85 kVp and 21 mAs. CBCT images were obtained two times with and two times without MAR activation. The DICOM format of the CBCT images was imported to ImageJ software (version 1.54), and the contrast-to-noise ratio (CNR) was calculated and compared for each bracket in 15 and 20 mm distances and 20, 40, and 90 degrees on each side. Statistical analysis was performed using the t test (α = 0.05). CNR values of different distances and different teeth were not significantly different between the two MAR modes (p > 0.05). MAR activation had a significant impact in increasing CNR and reducing artifacts only when brackets were in palatal (p = 0.03). In the other bracket and wire positions, the effect of the MAR algorithm on CNR was not significant (p > 0.05). In conclusion, MAR activation significantly increased CNR, but only when the brackets were in a palatal position. In the other bracket and wire positions, the effect of the MAR algorithm is not significant. Full article

(This article belongs to the Special Issue Deep Learning for Medical Imaging Diagnosis)

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17 pages, 1842 KiB

Open AccessArticle

Resting-State Functional Connectivity Difference in Alzheimer’s Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement

by Ramesh Kumar Lama and Goo-Rak Kwon

Diagnostics 2023, 13(19), 3074; https://doi.org/10.3390/diagnostics13193074 - 28 Sep 2023

Viewed by 1423

Abstract

The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer’s disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pearson’s correlation-based functional connectivity was used to construct the brain network. The study found that connections in the sensory motor network (SMN), dorsal attention network (DAN), salience network (SAN), default mode network (DMN), and cerebral network were severely affected in AD and MCI. The disruption in these networks may serve as potential biomarkers for distinguishing AD and MCI from HC. The study suggests that alterations in functional connectivity in these networks may contribute to cognitive deficits observed in AD and MCI. Additionally, a negative correlation was observed between the global clinical dementia rating (CDR) score and the Z-score of functional connectivity within identified clusters in AD subjects. These findings provide compelling evidence suggesting that the neurodegenerative disruption of functional magnetic resonance imaging (fMRI) connectivity is extensively distributed across multiple networks in individuals diagnosed with AD. Full article

(This article belongs to the Special Issue Deep Learning for Medical Imaging Diagnosis)

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13 pages, 2805 KiB

Open AccessArticle

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

by Tuba Ekmekyapar and Burak Taşcı

Diagnostics 2023, 13(19), 3030; https://doi.org/10.3390/diagnostics13193030 - 23 Sep 2023

Cited by 4 | Viewed by 1668

Abstract

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that prominently affects young adults due to its debilitating nature. The pathogenesis of the disease is focused on the inflammation and neurodegeneration processes. Inflammation is associated with relapses, while neurodegeneration emerges in the progressive stages of the disease. Magnetic resonance imaging (MRI) is commonly used for the diagnosis of MS, and guidelines such as the McDonald criteria are available. MRI is an essential tool to demonstrate the spatial distribution and changes over time in the disease. This study discusses the use of image processing techniques for the diagnosis of MS and specifically combines the MobileNetV2 network with exemplar-based learning, IMrMr feature selection, and K-Nearest Neighbors (KNN) classification methods. Experiments conducted on two different datasets (Dataset 1 and Dataset 2) demonstrate that these methods provide high accuracy in diagnosing MS. Dataset 1 comprises 128 patients with 706 MRI images, 131 MS patients with 667 MRI images, and 150 healthy control subjects with 1373 MRI images. Dataset 2 includes an MS group with 650 MRI images and a healthy control group with 676 MRI images. The results of the study include 10-fold cross-validation results performed on different image sections (Axial, Sagittal, and Hybrid) for Dataset 1. Accuracy rates of 99.76% for Axial, 99.48% for Sagittal, and 98.02% for Hybrid sections were achieved. Furthermore, 100% accuracy was achieved on Dataset 2. In conclusion, this study demonstrates the effective use of powerful image processing methods such as the MobileNetV2 network and exemplar-based learning for the diagnosis of MS. These findings suggest that these methods can be further developed in future research and offer significant potential for clinical applications in the diagnosis and monitoring of MS. Full article

(This article belongs to the Special Issue Deep Learning for Medical Imaging Diagnosis)

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18 pages, 18302 KiB

Open AccessArticle

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

by Mehmet Akif Cifci, Sadiq Hussain and Peren Jerfi Canatalay

Diagnostics 2023, 13(6), 1025; https://doi.org/10.3390/diagnostics13061025 - 8 Mar 2023

Cited by 6 | Viewed by 2459

Abstract

The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events. Full article

(This article belongs to the Special Issue Deep Learning for Medical Imaging Diagnosis)

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9 pages, 3773 KiB

Open AccessBrief Report

Impact of Various Non-Contrast-Enhanced MRA Techniques on Lumen Visibility in Vascular Flow Models with a Surpass Evolve Flow Diverter

by Yigit Ozpeynirci, Margarita Gorodezky, Augusto Fava Sanches, Sagar Mandava, Ana Beatriz Solana and Thomas Liebig

Diagnostics 2024, 14(11), 1146; https://doi.org/10.3390/diagnostics14111146 - 30 May 2024

Viewed by 635

Abstract

Background: Silent MRA has shown promising results in evaluating the stents used for intracranial aneurysm treatment. A deep learning-based denoising and deranging algorithm was recently introduced by GE HealthCare. The purpose of this study was to compare the performance of several MRA techniques regarding lumen visibility in silicone models with flow diverter stents. Methods: Two Surpass Evolve stents of different sizes were implanted in two silicone tubes. The tubes were placed in separate boxes in the straight position and in two different curve configurations and connected to a pulsatile pump to construct a flow loop. Using a 3.0T MRI scanner, TOF and silent MRA images were acquired, and deep learning reconstruction was applied to the silent MRA dataset. The intraluminal signal intensity in the stent (SI_in-stent), in the tube outside the stent (SI_vessel), and of the background (SI_bg) were measured for each scan. Results: The SI_in-stent/SI_bg and SI_in-stent/SI_v ratios were higher in the silent scans and DL-based reconstructions than in the TOF images. The stent tips created severe artefacts in the TOF images, which could not be observed in the silent scans. Conclusions: Our study demonstrates that the DL reconstruction algorithm improves the quality of the silent MRA technique in evaluating the flow diverter stent patency. Full article

(This article belongs to the Special Issue Deep Learning for Medical Imaging Diagnosis)

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8 pages, 2050 KiB

Open AccessBrief Report

MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data

by Dennis Hartmann, Verena Schmid, Philip Meyer, Florian Auer, Iñaki Soto-Rey, Dominik Müller and Frank Kramer

Diagnostics 2023, 13(16), 2618; https://doi.org/10.3390/diagnostics13162618 - 8 Aug 2023

Viewed by 1386

Abstract

Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses when it comes to assessing certain edge cases. These limitations arise when images with a very small region of interest or without a region of interest at all are assessed. As a solution to these limitations, we propose a new medical image segmentation metric: MISm. This metric is a composition of the Dice similarity coefficient and the weighted specificity. MISm was investigated for definition gaps, an appropriate scoring gradient, and different weighting coefficients used to propose a constant value. Furthermore, an evaluation was performed by comparing the popular metrics in the medical image segmentation and MISm using images of magnet resonance tomography from several fictitious prediction scenarios. Our analysis shows that MISm can be applied in a general way and thus also covers the mentioned edge cases, which are not covered by other metrics, in a reasonable way. In order to allow easy access to MISm and therefore widespread application in the community, as well as reproducibility of experimental results, we included MISm in the publicly available evaluation framework MISeval. Full article

(This article belongs to the Special Issue Deep Learning for Medical Imaging Diagnosis)

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