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Diagnostics
Special Issues
Medical Image Analysis: Current and Future Trends
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Medical Image Analysis: Current and Future Trends

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 May 2023) | Viewed by 12718

Special Issue Editor

Prof. Dr. Muhammad Usman Akram

E-Mail Website
Guest Editor
Department of Computer & Software Engineering, National University of Sciences and Technology Pakistan, Islamabad, Pakistan
Interests: medical image processing; pattern recognition; Machine Learning; signal processing; feature extraction; digital image processing; classification; pattern classification; object recognition; feature selection

Special Issue Information

Dear Colleagues,

The analysis of medical images is crucial in today’s healthcare systems. Medical imaging modalities range from ultrasound to X-rays, magnetic resonance imaging, etc. Computer-aided diagnostic tools (CAD) have been utilized to enlighten us on the likely disease process, but analysis and diagnosis based on a single image are generally challenging. CAD tools have utilized advancements in image analysis and artificial intelligence. In the last couple of decades, AI- and deep-learning-based algorithms have been widely used to design state-of-the-art medical image analysis solutions. These include the analysis of different medical imaging modalities for prognosis, diagnosis, prediction, progress monitoring, etc. of different diseases.

AI applications hold significant potential for enhancing and streamlining existing tasks in medical practice, such as diagnosis, treatment, prevention, progression, and personalized care. This Special Issue on “Medical Image Analysis: Current and Future Trends” will provide a venue for presenting recent findings in the area of medical images, with special emphasis on the application of artificial intelligence and deep learning. Specifically, it invites researchers from the biomedical and AI domains to submit original research and systematic reviews with applications towards medical image analysis, including but not limited to: 

  • Medical image analysis and diagnostics;
  • Decision support systems based on medical image analysis;
  • Explainable and trustworthy AI in diagnostics;
  • Transformers for medical image analysis;
  • Machine learning/deep learning in medical image analysis;
  • Radiological reporting using natural language processing/generation.

Prof. Dr. Muhammad Usman Akram
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.

Published Papers (5 papers)

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Research

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16 pages, 669 KiB  
Article
Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding
by Noor ul Huda, Anum Abdul Salam, Norah Saleh Alghamdi, Jahan Zeb and Muhammad Usman Akram
Diagnostics 2023, 13(13), 2231; https://doi.org/10.3390/diagnostics13132231 - 30 Jun 2023
Viewed by 1246
Abstract
Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood [...] Read more.
Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. In this paper, a novel method is proposed for the detection and grading of neovascularization. The proposed system first performs pre-processing on input retinal images to enhance the vascular pattern, followed by blood vessel segmentation and optic disc localization. Then various features are tested on the candidate regions with different thresholds. In this way, positive and negative advanced diabetic retinopathy cases are separated. Optic disc coordinates are applied for the grading of neovascularization as NVD or NVE. The proposed algorithm improves the quality of automated diagnostic systems by eliminating normal blood vessels and exudates that might cause hindrances in accurate disease detection, thus resulting in more accurate detection of abnormal blood vessels. The evaluation of the proposed system has been carried out using performance parameters such as sensitivity, specificity, accuracy, and positive predictive value (PPV) on a publicly available standard retinal image database and one of the locally available databases. The proposed algorithm gives an accuracy of 98.5% and PPV of 99.8% on MESSIDOR and an accuracy of 96.5% and PPV of 100% on the local database. Full article
(This article belongs to the Special Issue Medical Image Analysis: Current and Future Trends)
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12 pages, 1878 KiB  
Article
Site-Specific Differences in Bone Mineral Density of Proximal Femur Correlate with the Type of Hip Fracture
by Ning Li, Yi Yuan, Lu Yin, Minghui Yang, Yandong Liu, Wenshuang Zhang, Kangkang Ma, Fengyun Zhou, Zitong Cheng, Ling Wang and Xiaoguang Cheng
Diagnostics 2023, 13(11), 1877; https://doi.org/10.3390/diagnostics13111877 - 27 May 2023
Cited by 2 | Viewed by 1202
Abstract
The aim of this study was to investigate whether site-specific differences in bone mineral density (BMD) of proximal femur correlate with the type of hip fracture using quantitative computed tomography. Femoral neck (FN) fractures were classified as nondisplaced or displaced subtypes. Intertrochanteric (IT) [...] Read more.
The aim of this study was to investigate whether site-specific differences in bone mineral density (BMD) of proximal femur correlate with the type of hip fracture using quantitative computed tomography. Femoral neck (FN) fractures were classified as nondisplaced or displaced subtypes. Intertrochanteric (IT) fractures were classified as A1, A2, or A3. The severe hip fractures were identified as displaced FN fractures or unstable IT fractures (A2 and A3). In total, 404 FN fractures (89 nondisplaced and 317 displaced) and 189 IT fractures (76 A1, 90 A2, and 23 A3) were enrolled. Areal BMD (aBMD) and volumetric BMD (vBMD) were measured in the regions of total hip (TH), trochanter (TR), FN, and IT of the contralateral unfractured femur. IT fractures exhibited lower BMD than FN fractures (all p ≤ 0.01). However, unstable IT fractures had higher BMD compared with stable ones (p < 0.01). After adjusting for covariates, higher BMD in TH and IT were associated with IT A2 (A1 vs. A2: odds ratios (ORs) from 1.47 to 1.69, all p < 0.01). Low bone measurements were risk factors for stable IT fractures (IT A1 vs. FN fracture subtypes: ORs from 0.40 to 0.65, all p < 0.01). There are substantial site-specific differences in BMD between IT fractures A1 and displaced FN fractures. Higher bone density was associated with unstable IT fracture when compared with stable ones. The understanding of biomechanics of various fracture types could help to improve the clinical management of these patients. Full article
(This article belongs to the Special Issue Medical Image Analysis: Current and Future Trends)
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17 pages, 4332 KiB  
Article
Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach
by Ayesha Shoukat, Shahzad Akbar, Syed Ale Hassan, Sajid Iqbal, Abid Mehmood and Qazi Mudassar Ilyas
Diagnostics 2023, 13(10), 1738; https://doi.org/10.3390/diagnostics13101738 - 14 May 2023
Cited by 13 | Viewed by 3003
Abstract
Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at [...] Read more.
Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions. Full article
(This article belongs to the Special Issue Medical Image Analysis: Current and Future Trends)
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Review

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18 pages, 2486 KiB  
Review
Federated Learning for Medical Image Analysis with Deep Neural Networks
by Sajid Nazir and Mohammad Kaleem
Diagnostics 2023, 13(9), 1532; https://doi.org/10.3390/diagnostics13091532 - 24 Apr 2023
Cited by 17 | Viewed by 4332
Abstract
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, [...] Read more.
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model’s parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions. Full article
(This article belongs to the Special Issue Medical Image Analysis: Current and Future Trends)
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Other

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8 pages, 714 KiB  
Case Report
Prenatal Diagnosis and Fetopsy Validation of Complete Atrioventricular Septal Defects Using the Fetal Intelligent Navigation Echocardiography Method
by Paola Veronese, Alvise Guariento, Claudia Cattapan, Marny Fedrigo, Maria Teresa Gervasi, Annalisa Angelini, Arianna Riva and Vladimiro Vida
Diagnostics 2023, 13(3), 456; https://doi.org/10.3390/diagnostics13030456 - 26 Jan 2023
Cited by 5 | Viewed by 1673
Abstract
(1) Background: Artificial Intelligence (AI) is a modern tool with numerous applications in the medical field. The case series reported here aimed to investigate the diagnostic performance of the fetal intelligent navigation echocardiography (FINE) method applied for the first time in the prenatal [...] Read more.
(1) Background: Artificial Intelligence (AI) is a modern tool with numerous applications in the medical field. The case series reported here aimed to investigate the diagnostic performance of the fetal intelligent navigation echocardiography (FINE) method applied for the first time in the prenatal identification of atrioventricular septal defects (AVSD). This congenital heart disease (CHD) is associated with extracardiac anomalies and chromosomal abnormalities. Therefore, an early diagnosis is essential to advise parents and make adequate treatment decisions. (2) Methods: Four fetuses diagnosed with AVSD via two-dimensional (2D) ultrasound examination in the second trimester were enrolled. In all cases, the parents chose to terminate the pregnancy. Since the diagnosis of AVSD with 2D ultrasound may be missed, one or more four-dimensional (4D) spatiotemporal image correlation (STIC) volume datasets were obtained from a four-chamber view. The manual navigation enabled by the software is time-consuming and highly operator-dependent. (3) Results: FINE was applied to these volumes and nine standard fetal echocardiographic views were generated and optimized automatically, using the assistance of the virtual intelligent sonographer (VIS). Here, 100% of the four-chamber views, and after the VISA System application the five-chamber views, of the diagnostic plane showed the atrioventricular septal defect and a common AV valve. The autopsies of the fetuses confirmed the ultrasound results. (4) Conclusions: By applying intelligent navigation technology to the STIC volume datasets, 100% of the AVSD diagnoses were detected. Full article
(This article belongs to the Special Issue Medical Image Analysis: Current and Future Trends)
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