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Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional...
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Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models

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: 30 June 2024 | Viewed by 9845

Special Issue Editor

Prof. Dr. Stanislav Makhanov

E-Mail Website
Guest Editor
Sirindhorn International Institute of Technology, Thammasat University, Rangsit Campus 99 Moo 18, Km. 41, Paholyothin Highway Khlong Luang, Pathum Thani 12120, Thailand
Interests: medical image processing; pattern recognition; artificial life; artificial intelligence

Special Issue Information

Dear Colleagues,

On behalf of the Diagnostics Editorial Team, I am delighted to invite contributions to the Special Issue “When Conventional Models Meet AI”.

AI has revolutionized the medical industry and has become an unstoppable force, delivering numerous benefits. However, even in the presence of huge amounts of data collected by sensors, IoT, and imaging devices, AI often fails when applied to unknown data. For example, many neural networks are able to find signs of cancer in mammograms but cannot tell the difference between a rectangle and a triangle. Therefore, this Issue invites research on diagnostic and monitoring systems that combine AI with applications representing different aspects of human intelligence. Papers could include topics such as the hybrids of the neural networks and super pixels or deep learning and level-set methods in image processing, i.e., when conventional models meet AI.  

We accept research articles, position papers, short communications, critical reviews, and comments. Research involving case studies, newly discovered data, and public medical databases are also welcomed.

Papers may concern (but are not limited to) AI and AI hybrids for health monitoring and diagnosis, medical image segmentation, pattern recognition, data fusion, data mining, transfer learning, deep reinforcement learning, genetic and biologically inspired algorithms, artificial life, and edge and cloud computing.

I look forward to receiving your contributions.

Prof. Dr. Stanislav Makhanov
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

  • artificial intelligence
  • diagnostics
  • medical image processing

Published Papers (7 papers)

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Research

Jump to: Review

14 pages, 4142 KiB  
Article
Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders
by Zhengchun Lu, Mayu Morita, Tyler S. Yeager, Yunpeng Lyu, Sophia Y. Wang, Zhigang Wang and Guang Fan
Diagnostics 2024, 14(4), 420; https://doi.org/10.3390/diagnostics14040420 - 14 Feb 2024
Viewed by 955
Abstract
Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow [...] Read more.
Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density–phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4−/CD8− double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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11 pages, 1255 KiB  
Article
Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model
by So Jin Yoon, Donghyun Kim, Sook Hyun Park, Jung Ho Han, Joohee Lim, Jeong Eun Shin, Ho Seon Eun, Soon Min Lee and Min Soo Park
Diagnostics 2023, 13(24), 3627; https://doi.org/10.3390/diagnostics13243627 - 08 Dec 2023
Viewed by 820
Abstract
Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 [...] Read more.
Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 VLBW infants, born between 2013 and 2017 in four hospitals, were included. PGF was defined as a decrease in z-score between birth and discharge that was greater than 1.28. Feature selection and addition were performed to improve the accuracy of prediction at four different time points, including 0, 7, 14, and 28 days after birth. A total of 12 features with high contribution at all time points by feature importance were decided upon, and good performance was shown as an area under the receiver operating characteristic curve (AUROC) of 0.78 at 7 days. After adding weight change to the 12 features—which included sex, gestational age, birth weight, small for gestational age, maternal hypertension, respiratory distress syndrome, duration of invasive ventilation, duration of non-invasive ventilation, patent ductus arteriosus, sepsis, use of parenteral nutrition, and reach at full enteral nutrition—the AUROC at 7 days after birth was shown as 0.84. Our prediction model for PGF performed well at early detection. Its potential clinical application as a supplemental tool could be helpful for reducing PGF and improving child health. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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23 pages, 2775 KiB  
Article
Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation
by Nalan Karunanayake, Samart Moodleah and Stanislav S. Makhanov
Diagnostics 2023, 13(24), 3611; https://doi.org/10.3390/diagnostics13243611 - 06 Dec 2023
Viewed by 918
Abstract
A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the [...] Read more.
A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the contour of the tumor. The agents move similarly to robots navigating in the unknown environment with the goal of maximizing the rewards. The individual agent does not know the goal of the entire population. However, since the robots communicate, the model is able to understand the global information and fit the irregular boundaries of complicated objects. Combining the RL with a neural network makes it possible to automatically learn and select the local features. In particular, the agents handle the edge leaks and artifacts typical for the US images. The proposed model outperforms 13 state-of-the-art algorithms, including selected deep learning models and their modifications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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14 pages, 3009 KiB  
Article
An Automated Grading System Based on Topological Features for the Evaluation of Corneal Fluorescein Staining in Dry Eye Disease
by Jun Feng, Zi-Kai Ren, Kai-Ni Wang, Hao Guo, Yi-Ran Hao, Yuan-Chao Shu, Lei Tian, Guang-Quan Zhou and Ying Jie
Diagnostics 2023, 13(23), 3533; https://doi.org/10.3390/diagnostics13233533 - 26 Nov 2023
Cited by 1 | Viewed by 975
Abstract
Background: Corneal fluorescein staining is a key biomarker in evaluating dry eye disease. However, subjective scales of corneal fluorescein staining are lacking in consistency and increase the difficulties of an accurate diagnosis for clinicians. This study aimed to propose an automatic machine learning-based [...] Read more.
Background: Corneal fluorescein staining is a key biomarker in evaluating dry eye disease. However, subjective scales of corneal fluorescein staining are lacking in consistency and increase the difficulties of an accurate diagnosis for clinicians. This study aimed to propose an automatic machine learning-based method for corneal fluorescein staining evaluation by utilizing prior information about the spatial connection and distribution of the staining region. Methods: We proposed an end-to-end automatic machine learning-based classification model that consists of staining region identification, feature signature construction, and machine learning-based classification, which fully scrutinizes the multiscale topological features together with conventional texture and morphological features. The proposed model was evaluated using retrospective data from Beijing Tongren Hospital. Two masked ophthalmologists scored images independently using the Sjögren’s International Collaborative Clinical Alliance Ocular Staining Score scale. Results: A total of 382 images were enrolled in the study. A signature with six topological features, two textural features, and two morphological features was constructed after feature extraction and selection. Support vector machines showed the best classification performance (accuracy: 82.67%, area under the curve: 96.59%) with the designed signature. Meanwhile, topological features contributed more to the classification, compared with other features. According to the distribution and correlation with features and scores, topological features performed better than others. Conclusions: An automatic machine learning-based method was advanced for corneal fluorescein staining evaluation. The topological features in presenting the spatial connectivity and distribution of staining regions are essential for an efficient corneal fluorescein staining evaluation. This result implies the clinical application of topological features in dry-eye diagnosis and therapeutic effect evaluation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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16 pages, 7221 KiB  
Article
Respiratory Diaphragm Motion-Based Asynchronization and Limitation Evaluation on Chronic Obstructive Pulmonary Disease
by Xingyu Zhou, Chen Ye, Yuma Iwao, Takayuki Okamoto, Naoko Kawata, Ayako Shimada and Hideaki Haneishi
Diagnostics 2023, 13(20), 3261; https://doi.org/10.3390/diagnostics13203261 - 20 Oct 2023
Viewed by 803
Abstract
Background: Chronic obstructive pulmonary disease (COPD) typically causes airflow blockage and breathing difficulties, which may result in the abnormal morphology and motion of the lungs or diaphragm. Purpose: This study aims to quantitatively evaluate respiratory diaphragm motion using a thoracic sagittal magnetic resonance [...] Read more.
Background: Chronic obstructive pulmonary disease (COPD) typically causes airflow blockage and breathing difficulties, which may result in the abnormal morphology and motion of the lungs or diaphragm. Purpose: This study aims to quantitatively evaluate respiratory diaphragm motion using a thoracic sagittal magnetic resonance imaging (MRI) series, including motion asynchronization and limitations. Method: First, the diaphragm profile is extracted using a deep-learning-based field segmentation approach. Next, by measuring the motion waveforms of each position in the extracted diaphragm profile, obvious differences in the independent respiration cycles, such as the period and peak amplitude, are verified. Finally, focusing on multiple breathing cycles, the similarity and amplitude of the motion waveforms are evaluated using the normalized correlation coefficient (NCC) and absolute amplitude. Results and Contributions: Compared with normal subjects, patients with severe COPD tend to have lower NCC and absolute amplitude values, suggesting motion asynchronization and limitation of their diaphragms. Our proposed diaphragmatic motion evaluation method may assist in the diagnosis and therapeutic planning of COPD. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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12 pages, 3674 KiB  
Article
The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain
by Dittapong Songsaeng, Poonsuta Nava-apisak, Jittsupa Wongsripuemtet, Siripra Kingchan, Phuriwat Angkoondittaphong, Phattaranan Phawaphutanon and Akara Supratak
Diagnostics 2023, 13(17), 2840; https://doi.org/10.3390/diagnostics13172840 - 01 Sep 2023
Cited by 1 | Viewed by 2596
Abstract
Diagnosing normal-pressure hydrocephalus (NPH) via non-contrast computed tomography (CT) brain scans is presently a formidable task due to the lack of universally agreed-upon standards for radiographic parameter measurement. A variety of radiological parameters, such as Evans’ index, narrow sulci at high parietal convexity, [...] Read more.
Diagnosing normal-pressure hydrocephalus (NPH) via non-contrast computed tomography (CT) brain scans is presently a formidable task due to the lack of universally agreed-upon standards for radiographic parameter measurement. A variety of radiological parameters, such as Evans’ index, narrow sulci at high parietal convexity, Sylvian fissures’ dilation, focally enlarged sulci, and more, are currently measured by radiologists. This study aimed to enhance NPH diagnosis by comparing the accuracy, sensitivity, specificity, and predictive values of radiological parameters, as evaluated by radiologists and AI methods, utilizing cerebrospinal fluid volumetry. Results revealed a sensitivity of 77.14% for radiologists and 99.05% for AI, with specificities of 98.21% and 57.14%, respectively, in diagnosing NPH. Radiologists demonstrated NPV, PPV, and an accuracy of 82.09%, 97.59%, and 88.02%, while AI reported 98.46%, 68.42%, and 77.42%, respectively. ROC curves exhibited an area under the curve of 0.954 for radiologists and 0.784 for AI, signifying the diagnostic index for NPH. In conclusion, although radiologists exhibited superior sensitivity, specificity, and accuracy in diagnosing NPH, AI served as an effective initial screening mechanism for potential NPH cases, potentially easing the radiologists’ burden. Given the ongoing AI advancements, it is plausible that AI could eventually match or exceed radiologists’ diagnostic prowess in identifying hydrocephalus. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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Review

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18 pages, 3624 KiB  
Review
A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images
by Yar Zar Tun and Pakinee Aimmanee
Diagnostics 2023, 13(22), 3407; https://doi.org/10.3390/diagnostics13223407 - 09 Nov 2023
Cited by 2 | Viewed by 1145
Abstract
Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal [...] Read more.
Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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