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Artificial Intelligence in Auto-Diagnosis and Clinical Applications

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 21277

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Dr. Teng Grace Zhang

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Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, No. 3 Sassoon Road, Hong Kong
Interests: bioengineering; digital orthopedics; clinical medicine; spine deformity; disease progression prediction; artificial intelligence and modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Jason Pui Yin Cheung

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Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, No. 3 Sassoon Road, Hong Kong
Interests: computer-aided medicine; computational orthopedics; clinical medicine; pediatric spine deformity; scoliosis genetics; artificial intelligence and modeling; medical imaging and clinical phenotyping
Special Issues, Collections and Topics in MDPI journals

Dr. Tianjiao Zeng

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School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: computational imaging; inverse imaging problems; deep learning; neuroimaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, artificial intelligence (AI) techniques have revolutionized many fields, including healthcare, disease diagnosis, staging, treatment, surgical planning, etc., and they have the potential to transform the way we approach medical diagnosis and treatment. This boost in AI technology has led to great advancements in the translation of AI algorithms from research to real clinical practice. In this context, AI has demonstrated itself to be a powerful tool to gain novel insights, pursue different objectives, and seek alternative solutions that are applicable to solve medical and clinical problems in a more accurate, instant, and automated way. This Special Issue, therefore, seeks original contributions (articles, reviews, comments, etc.) involving the processing of medical and clinical images, AI algorithm development, medical device and software development, and image-quality improvements.

Topics include, but are not limited to, the following:

Medical data processing and analysis;
Deep learning and machine learning for bioengineering;
Deep learning and machine learning for clinical applications;
Biomedical and health informatics;
Synthetic medical image generation;
Medical auto-analysis and disease-progression predictions;
Explainable AI in medicine;
Artificial intelligence medical systems.

Dr. Teng Grace Zhang
Dr. Jason Pui Yin Cheung
Dr. Tianjiao Zeng
Guest Editors

Manuscript Submission Information

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Keywords

artificial intelligence
deep learning
machine learning
computer-aided analysis
progress prediction
image processing
interpretability for collecting review reports

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Published Papers (11 papers)

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Research

29 pages, 7309 KiB

Open AccessArticle

Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI

by Hossam Magdy Balaha, Sarah M. Ayyad, Ahmed Alksas, Mohamed Shehata, Ali Elsorougy, Mohamed Ali Badawy, Mohamed Abou El-Ghar, Ali Mahmoud, Norah Saleh Alghamdi, Mohammed Ghazal, Sohail Contractor and Ayman El-Baz

Bioengineering 2024, 11(6), 629; https://doi.org/10.3390/bioengineering11060629 - 19 Jun 2024

Cited by 2 | Viewed by 1469

Abstract

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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19 pages, 5868 KiB

Open AccessArticle

OMGMed: Advanced System for Ocular Myasthenia Gravis Diagnosis via Eye Image Segmentation

by Jianqiang Li, Chujie Zhu, Mingming Zhao, Xi Xu, Linna Zhao, Wenxiu Cheng, Suqin Liu, Jingchen Zou, Ji-Jiang Yang and Jian Yin

Bioengineering 2024, 11(6), 595; https://doi.org/10.3390/bioengineering11060595 - 11 Jun 2024

Viewed by 1334

Abstract

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of “network structure” and “loss function”, and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of “nnUNet” network structure and “Cross-Entropy + Iou + Boundary” hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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21 pages, 4292 KiB

Open AccessArticle

Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting

by Jacob Ellison, Francesco Caliva, Pablo Damasceno, Tracy L. Luks, Marisa LaFontaine, Julia Cluceru, Anil Kemisetti, Yan Li, Annette M. Molinaro, Valentina Pedoia, Javier E. Villanueva-Meyer and Janine M. Lupo

Bioengineering 2024, 11(5), 497; https://doi.org/10.3390/bioengineering11050497 - 16 May 2024

Viewed by 1503

Abstract

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p < 0.05), and spatial regularization further improved performance when used with TL by 95th HD, Dice, and sensitivity (6.8%, 0.8%, 2.2%; p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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14 pages, 708 KiB

Open AccessArticle

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy

by Lizhang Xie, Lei Zhang, Ting Hu, Guangjun Li and Zhang Yi

Bioengineering 2024, 11(4), 362; https://doi.org/10.3390/bioengineering11040362 - 11 Apr 2024

Viewed by 1096

Abstract

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples’ model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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11 pages, 3728 KiB

Open AccessArticle

SpineHRformer: A Transformer-Based Deep Learning Model for Automatic Spine Deformity Assessment with Prospective Validation

by Moxin Zhao, Nan Meng, Jason Pui Yin Cheung, Chenxi Yu, Pengyu Lu and Teng Zhang

Bioengineering 2023, 10(11), 1333; https://doi.org/10.3390/bioengineering10111333 - 20 Nov 2023

Cited by 3 | Viewed by 1949

Abstract

The Cobb angle (CA) serves as the principal method for assessing spinal deformity, but manual measurements of the CA are time-consuming and susceptible to inter- and intra-observer variability. While learning-based methods, such as SpineHRNet+, have demonstrated potential in automating CA measurement, their accuracy can be influenced by the severity of spinal deformity, image quality, relative position of rib and vertebrae, etc. Our aim is to create a reliable learning-based approach that provides consistent and highly accurate measurements of the CA from posteroanterior (PA) X-rays, surpassing the state-of-the-art method. To accomplish this, we introduce SpineHRformer, which identifies anatomical landmarks, including the vertices of endplates from the 7th cervical vertebra (C7) to the 5th lumbar vertebra (L5) and the end vertebrae with different output heads, enabling the calculation of CAs. Within our SpineHRformer, a backbone HRNet first extracts multi-scale features from the input X-ray, while transformer blocks extract local and global features from the HRNet outputs. Subsequently, an output head to generate heatmaps of the endplate landmarks or end vertebra landmarks facilitates the computation of CAs. We used a dataset of 1934 PA X-rays with diverse degrees of spinal deformity and image quality, following an 8:2 ratio to train and test the model. The experimental results indicate that SpineHRformer outperforms SpineHRNet+ in landmark detection (Mean Euclidean Distance: 2.47 pixels vs. 2.74 pixels), CA prediction (Pearson correlation coefficient: 0.86 vs. 0.83), and severity grading (sensitivity: normal-mild; 0.93 vs. 0.74, moderate; 0.74 vs. 0.77, severe; 0.74 vs. 0.7). Our approach demonstrates greater robustness and accuracy compared to SpineHRNet+, offering substantial potential for improving the efficiency and reliability of CA measurements in clinical settings. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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15 pages, 3645 KiB

Open AccessArticle

Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework

by Jun Huang, Aiyue Huang, Ruqin Xu, Musheng Wu, Peng Wang and Qing Wang

Bioengineering 2023, 10(11), 1319; https://doi.org/10.3390/bioengineering10111319 - 16 Nov 2023

Cited by 2 | Viewed by 1565

Abstract

This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net decoder, batch normalization, attention block and deepened convolution layer based on the U-Net backbone. Then, a VABC-UNet-based assessment framework was established for automatic segmentation, classification, and evaluation of valvular regurgitations. A total of 315 color Doppler echocardiography images of MR and/or TR in an apical four-chamber view were collected, including 35 images in the test dataset and 280 images in the training dataset. In comparison with the classic U-Net and VGG16-UNet models, the segmentation performance of the VABC-UNet model was evaluated via four metrics: Dice, Jaccard, Precision, and Recall. According to the features of regurgitation jet and atrium, the regurgitation could automatically be classified into MR or TR, and evaluated to mild, moderate, moderate–severe, or severe grade by the framework. The results show that the VABC-UNet model has a superior performance in the segmentation of valvular regurgitation jets and atria to the other two models and consequently a higher accuracy of classification and evaluation. There were fewer pseudo- and over-segmentations by the VABC-UNet model and the values of the metrics significantly improved (p < 0.05). The proposed VABC-UNet-based framework achieves automatic segmentation, classification, and evaluation of MR and TR, having potential to assist radiologists in clinical decision making of the regurgitations in valvular heart diseases. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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24 pages, 1634 KiB

Open AccessArticle

RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis

by Hyunil Kim, Tae-Yeong Kwak, Hyeyoon Chang, Sun Woo Kim and Injung Kim

Bioengineering 2023, 10(11), 1279; https://doi.org/10.3390/bioengineering10111279 - 2 Nov 2023

Cited by 3 | Viewed by 1977

Abstract

We propose a novel transfer learning framework for pathological image analysis, the Response-based Cross-task Knowledge Distillation (RCKD), which improves the performance of the model by pretraining it on a large unlabeled dataset guided by a high-performance teacher model. RCKD first pretrains a student model to predict the nuclei segmentation results of the teacher model for unlabeled pathological images, and then fine-tunes the pretrained model for the downstream tasks, such as organ cancer sub-type classification and cancer region segmentation, using relatively small target datasets. Unlike conventional knowledge distillation, RCKD does not require that the target tasks of the teacher and student models be the same. Moreover, unlike conventional transfer learning, RCKD can transfer knowledge between models with different architectures. In addition, we propose a lightweight architecture, the Convolutional neural network with Spatial Attention by Transformers (CSAT), for processing high-resolution pathological images with limited memory and computation. CSAT exhibited a top-1 accuracy of 78.6% on ImageNet with only 3M parameters and 1.08 G multiply-accumulate (MAC) operations. When pretrained by RCKD, CSAT exhibited average classification and segmentation accuracies of 94.2% and 0.673 mIoU on six pathological image datasets, which is 4% and 0.043 mIoU higher than EfficientNet-B0, and 7.4% and 0.006 mIoU higher than ConvNextV2-Atto pretrained on ImageNet, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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15 pages, 2637 KiB

Open AccessArticle

Hybrid Machine Learning Framework for Multistage Parkinson’s Disease Classification Using Acoustic Features of Sustained Korean Vowels

by S. I. M. M. Raton Mondol, Ryul Kim and Sangmin Lee

Bioengineering 2023, 10(8), 984; https://doi.org/10.3390/bioengineering10080984 - 20 Aug 2023

Cited by 3 | Viewed by 1962

Abstract

Recent research has achieved a great classification rate for separating healthy people from those with Parkinson’s disease (PD) using speech and the voice. However, these studies have primarily treated early and advanced stages of PD as equal entities, neglecting the distinctive speech impairments and other symptoms that vary across the different stages of the disease. To address this limitation, and improve diagnostic precision, this study assesses the selected acoustic features of dysphonia, as they relate to PD and the Hoehn and Yahr stages, by combining various preprocessing techniques and multiple classification algorithms, to create a comprehensive and robust solution for classification tasks. The dysphonia features extracted from the three sustained Korean vowels /아/(a), /이/(i), and /우/(u) exhibit diversity and strong correlations. To address this issue, the analysis of variance F-Value feature selection classifier from scikit-learn was employed, to identify the topmost relevant features. Additionally, to overcome the class imbalance problem, the synthetic minority over-sampling technique was utilized. To ensure fair comparisons, and mitigate the influence of individual classifiers, four commonly used machine learning classifiers, namely random forest (RF), support vector machine (SVM), k-nearest neighbor (kNN), and multi-layer perceptron (MLP), were employed. This approach enables a comprehensive evaluation of the feature extraction methods, and minimizes the variance in the final classification models. The proposed hybrid machine learning pipeline using the acoustic features of sustained vowels efficiently detects the early and mid-advanced stages of PD with a detection accuracy of 95.48%, and with a detection accuracy of 86.62% for the 4-stage, and a detection accuracy of 89.48% for the 3-stage classification of PD. This study successfully demonstrates the significance of utilizing the diverse acoustic features of dysphonia in the classification of PD and its stages. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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14 pages, 2233 KiB

Open AccessArticle

Effect of Cilostazol on Delayed Cerebral Infarction in Aneurysmal Subarachnoid Hemorrhage Using Explainable Predictive Modeling

by Kwang Hyeon Kim, Byung-Jou Lee and Hae-Won Koo

Bioengineering 2023, 10(7), 797; https://doi.org/10.3390/bioengineering10070797 - 3 Jul 2023

Cited by 2 | Viewed by 1899

Abstract

The studies interpreting DCI, a complication of SAH, and identifying correlations are very limited. This study aimed to investigate the effect of cilostazol on ACV and DCI after coil embolization for ruptured aneurysms (n = 432). A multivariate analysis was performed and explainable artificial intelligence approaches were used to analyze the contribution of cilostazol as a risk factor on the development of ACV and DCI with respect to global and local interpretation. The cilonimo group was significantly lower than the nimo group in ACV (13.5% vs. 29.3; p = 0.003) and DCI (7.9% vs. 20.7%; p = 0.006), respectively. In a multivariate logistic regression, the odds ratio for DCI for the cilonimo group, female sex, and aneurysm size was 0.556 (95% confidence interval (CI), 0.351–0.879; p = 0.012), 3.713 (95% CI, 1.683–8.191; p = 0.001), and 1.106 (95% CI, 1.008–1.214; p = 0.034). The risk of a DCI occurrence was significantly increased with an aneurysm size greater than 10 mm (max 80%). The mean AUC of the XGBoost and logistic regression models was 0.94 ± 0.03 and 0.95 ± 0.04, respectively. Cilostazol treatment combined with nimodipine could decrease the prevalence of ACV (13.5%) and DCI (7.9%) in patients with aSAH. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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12 pages, 2131 KiB

Open AccessArticle

Identification of Adolescent Menarche Status Using Biplanar X-ray Images: A Deep Learning-Based Method

by Linzhen Xie, Tenghui Ge, Bin Xiao, Xiaoguang Han, Qi Zhang, Zhongning Xu, Da He and Wei Tian

Bioengineering 2023, 10(7), 769; https://doi.org/10.3390/bioengineering10070769 - 26 Jun 2023

Viewed by 1327

Abstract

The purpose of this study is to develop an automated method for identifying the menarche status of adolescents based on EOS radiographs. We designed a deep-learning-based algorithm that contains a region of interest detection network and a classification network. The algorithm was trained and tested on a retrospective dataset of 738 adolescent EOS cases using a five-fold cross-validation strategy and was subsequently tested on a clinical validation set of 259 adolescent EOS cases. On the clinical validation set, our algorithm achieved accuracy of 0.942, macro precision of 0.933, macro recall of 0.938, and a macro F1-score of 0.935. The algorithm showed almost perfect performance in distinguishing between males and females, with the main classification errors found in females aged 12 to 14 years. Specifically for females, the algorithm had accuracy of 0.910, sensitivity of 0.943, and specificity of 0.855 in estimating menarche status, with an area under the curve of 0.959. The kappa value of the algorithm, in comparison to the actual situation, was 0.806, indicating strong agreement between the algorithm and the real-world scenario. This method can efficiently analyze EOS radiographs and identify the menarche status of adolescents. It is expected to become a routine clinical tool and provide references for doctors’ decisions under specific clinical conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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21 pages, 5493 KiB

Open AccessArticle

LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation

by Shuai Zhang and Yanmin Niu

Bioengineering 2023, 10(6), 712; https://doi.org/10.3390/bioengineering10060712 - 12 Jun 2023

Cited by 6 | Viewed by 3069

Abstract

In recent years, UNet and its improved variants have become the main methods for medical image segmentation. Although these models have achieved excellent results in segmentation accuracy, their large number of network parameters and high computational complexity make it difficult to achieve medical image segmentation in real-time therapy and diagnosis rapidly. To address this problem, we introduce a lightweight medical image segmentation network (LcmUNet) based on CNN and MLP. We designed LcmUNet’s structure in terms of model performance, parameters, and computational complexity. The first three layers are convolutional layers, and the last two layers are MLP layers. In the convolution part, we propose an LDA module that combines asymmetric convolution, depth-wise separable convolution, and an attention mechanism to reduce the number of network parameters while maintaining a strong feature-extraction capability. In the MLP part, we propose an LMLP module that helps enhance contextual information while focusing on local information and improves segmentation accuracy while maintaining high inference speed. This network also covers skip connections between the encoder and decoder at various levels. Our network achieves real-time segmentation results accurately in extensive experiments. With only 1.49 million model parameters and without pre-training, LcmUNet demonstrated impressive performance on different datasets. On the ISIC2018 dataset, it achieved an IoU of 85.19%, 92.07% recall, and 92.99% precision. On the BUSI dataset, it achieved an IoU of 63.99%, 79.96% recall, and 76.69% precision. Lastly, on the Kvasir-SEG dataset, LcmUNet achieved an IoU of 81.89%, 88.93% recall, and 91.79% precision. Full article

(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)

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