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Guan H, Wang Y, Niu P, Zhang Y, Zhang Y, Miao R, Fang X, Yin R, Zhao S, Liu J, Tian J. The role of machine learning in advancing diabetic foot: a review. Front Endocrinol (Lausanne) 2024; 15:1325434. [PMID: 38742201 PMCID: PMC11089132 DOI: 10.3389/fendo.2024.1325434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/09/2024] [Indexed: 05/16/2024] Open
Abstract
Background Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies. Objective This review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics. Methods A meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as "Machine Learning," "Diabetic Foot," "Diabetic Foot Ulcers," "Diabetic Foot Care," "Artificial Intelligence," and "Predictive Modeling." This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved. Results ML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care. Conclusion The review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.
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Affiliation(s)
- Huifang Guan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Ying Wang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Ping Niu
- Department of Encephalopathy, The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin, China
| | - Yuxin Zhang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanjiao Zhang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Runyu Miao
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xinyi Fang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ruiyang Yin
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Shuang Zhao
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Jun Liu
- Department of Hand Surgery, Second Hospital of Jilin University, Changchun, China
| | - Jiaxing Tian
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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Speeckaert R, Hoorens I, Lambert J, Speeckaert M, van Geel N. Beyond visual inspection: The value of infrared thermography in skin diseases, a scoping review. J Eur Acad Dermatol Venereol 2024. [PMID: 38251780 DOI: 10.1111/jdv.19796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/30/2023] [Indexed: 01/23/2024]
Abstract
Although warmth is a key sign of inflammatory skin lesions, an objective assessment and follow-up of the temperature changes are rarely done in dermatology. The recent availability of accurate, sensitive and cost-effective thermography devices has made the implementation of thermography in clinical settings feasible. The aim of this scoping review is to summarize the evidence around the value and pitfalls of infrared thermography (IRT) when used in the dermatology clinic. A systematic literature search was done for original articles using IRT in skin disorders. The results concerning the potential of IRT for diagnosis, severity staging and monitoring of skin diseases were collected. The data on the sensitivity and specificity of IRT were extracted. Numerous studies have investigated IRT in various skin diseases, revealing its significant value in wound management, skin infections (e.g. cellulitis), vascular abnormalities and deep skin inflammation (e.g. hidradenitis suppurativa). For other dermatological applications such as the interpretation of intradermal and patch allergy testing, hyper-/anhidrosis, erythromelalgia, cold urticaria and lymph node metastases more complex calculations, provocation tests or active cooling procedures are required. Dermatologists should be aware of a learning curve of IRT and recognize factors contributing to false positive and false negative results. Nonetheless, enough evidence is available to recommend IRT as a supplement to the clinical evaluation for the diagnosis, severity and follow-up of several skin diseases.
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Affiliation(s)
| | - Isabelle Hoorens
- Department of Dermatology, Ghent University Hospital, Ghent, Belgium
| | - Jo Lambert
- Department of Dermatology, Ghent University Hospital, Ghent, Belgium
| | | | - Nanja van Geel
- Department of Dermatology, Ghent University Hospital, Ghent, Belgium
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Arteaga-Marrero N, Hernández-Guedes A, Ortega-Rodríguez J, Ruiz-Alzola J. State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms. Biomedicines 2023; 11:3209. [PMID: 38137430 PMCID: PMC10741214 DOI: 10.3390/biomedicines11123209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
Diabetic foot ulcers represent the most frequently recognized and highest risk factor among patients affected by diabetes mellitus. The associated recurrent rate is high, and amputation of the foot or lower limb is often required due to infection. Analysis of infrared thermograms covering the entire plantar aspect of both feet is considered an emerging area of research focused on identifying at an early stage the underlying conditions that sustain skin and tissue damage prior to the onset of superficial wounds. The identification of foot disorders at an early stage using thermography requires establishing a subset of relevant features to reduce decision variability and data misinterpretation and provide a better overall cost-performance for classification. The lack of standardization among thermograms as well as the unbalanced datasets towards diabetic cases hinder the establishment of this suitable subset of features. To date, most studies published are mainly based on the exploitation of the publicly available INAOE dataset, which is composed of thermogram images of healthy and diabetic subjects. However, a recently released dataset, STANDUP, provided data for extending the current state of the art. In this work, an extended and more generalized dataset was employed. A comparison was performed between the more relevant and robust features, previously extracted from the INAOE dataset, with the features extracted from the extended dataset. These features were obtained through state-of-the-art methodologies, including two classical approaches, lasso and random forest, and two variational deep learning-based methods. The extracted features were used as an input to a support vector machine classifier to distinguish between diabetic and healthy subjects. The performance metrics employed confirmed the effectiveness of both the methodology and the state-of-the-art features subsequently extracted. Most importantly, their performance was also demonstrated when considering the generalization achieved through the integration of input datasets. Notably, features associated with the MCA and LPA angiosomes seemed the most relevant.
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Affiliation(s)
- Natalia Arteaga-Marrero
- Grupo Tecnología Médica IACTEC, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain; (J.O.-R.); (J.R.-A.)
| | - Abián Hernández-Guedes
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain;
- Instituto Universitario de Microelectrónica Aplicada (IUMA), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Jordan Ortega-Rodríguez
- Grupo Tecnología Médica IACTEC, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain; (J.O.-R.); (J.R.-A.)
| | - Juan Ruiz-Alzola
- Grupo Tecnología Médica IACTEC, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain; (J.O.-R.); (J.R.-A.)
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain;
- Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
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Pereira MG, Vilaça M, Braga D, Madureira A, Da Silva J, Santos D, Carvalho E. Healing profiles in patients with a chronic diabetic foot ulcer: An exploratory study with machine learning. Wound Repair Regen 2023; 31:793-803. [PMID: 38073283 DOI: 10.1111/wrr.13141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/21/2023] [Accepted: 10/16/2023] [Indexed: 12/26/2023]
Abstract
Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic and social challenges. Therefore, early identification of patients with a high-risk profile would be important to adequate treatment and more successful health outcomes. This study explores risk assessment profiles for DFU healing and healing prognosis, using machine learning predictive approaches and decision tree algorithms. Patients were evaluated at baseline (T0; N = 158) and 2 months later (T1; N = 108) on sociodemographic, clinical, biochemical and psychological variables. The performance evaluation of the models comprised F1-score, accuracy, precision and recall. Only profiles with F1-score >0.7 were selected for analysis. According to the two profiles generated for DFU healing, the most important predictive factors were illness representations on T1 IPQ-B (IPQ-B ≤ 9.5 and < 10.5) and the DFU duration (≤ 13 weeks). The two predictive models for DFU healing prognosis suggest that biochemical factors are the best predictors of a favorable healing prognosis, namely IL-6, microRNA-146a-5p and PECAM-1 at T0 and angiopoietin-2 at T1. Illness perception at T0 (IPQ-B ≤ 39.5) also emerged as a relevant predictor for healing prognosis. The results emphasize the importance of DFU duration, illness perception and biochemical markers as predictors of healing in chronic DFUs. Future research is needed to confirm and test the obtained predictive models.
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Affiliation(s)
- M Graça Pereira
- Psychology Research Center (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Margarida Vilaça
- Psychology Research Center (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Diogo Braga
- Interdisciplinary Studies Research Center (ISRC), ISEP, Porto, Portugal
| | - Ana Madureira
- Interdisciplinary Studies Research Center (ISRC), ISEP, Porto, Portugal
- ISEP, Polytechnic of Porto, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INOV), Lisboa, Portugal
| | - Jéssica Da Silva
- PhD Program in Experimental Biology and Biomedicine (PDBEB), Institute for Interdisciplinary Research, Coimbra, Portugal
- Center for Neuroscience and Cell Biology (CNC), Center for Innovative Biotechnology and Biomedicine (CIBB), University of Coimbra, Coimbra, Portugal
- Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Diana Santos
- PhD Program in Experimental Biology and Biomedicine (PDBEB), Institute for Interdisciplinary Research, Coimbra, Portugal
- Center for Neuroscience and Cell Biology (CNC), Center for Innovative Biotechnology and Biomedicine (CIBB), University of Coimbra, Coimbra, Portugal
- Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Eugénia Carvalho
- Center for Neuroscience and Cell Biology (CNC), Center for Innovative Biotechnology and Biomedicine (CIBB), University of Coimbra, Coimbra, Portugal
- Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
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Chun JW, Kim HS. The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use. J Korean Med Sci 2023; 38:e253. [PMID: 37550811 PMCID: PMC10412032 DOI: 10.3346/jkms.2023.38.e253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/12/2023] [Indexed: 08/09/2023] Open
Abstract
Artificial intelligence (AI)-based diagnostic technology using medical images can be used to increase examination accessibility and support clinical decision-making for screening and diagnosis. To determine a machine learning algorithm for diabetes complications, a literature review of studies using medical image-based AI technology was conducted using the National Library of Medicine PubMed, and the Excerpta Medica databases. Lists of studies using diabetes diagnostic images and AI as keywords were combined. In total, 227 appropriate studies were selected. Diabetic retinopathy studies using the AI model were the most frequent (85.0%, 193/227 cases), followed by diabetic foot (7.9%, 18/227 cases) and diabetic neuropathy (2.7%, 6/227 cases). The studies used open datasets (42.3%, 96/227 cases) or directly constructed data from fundoscopy or optical coherence tomography (57.7%, 131/227 cases). Major limitations in AI-based detection of diabetes complications using medical images were the lack of datasets (36.1%, 82/227 cases) and severity misclassification (26.4%, 60/227 cases). Although it remains difficult to use and fully trust AI-based imaging analysis technology clinically, it reduces clinicians' time and labor, and the expectations from its decision-support roles are high. Various data collection and synthesis data technology developments according to the disease severity are required to solve data imbalance.
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Affiliation(s)
- Ji-Won Chun
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hun-Sung Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
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Khan MM, Chowdhury MEH, Arefin ASMS, Podder KK, Hossain MSA, Alqahtani A, Murugappan M, Khandakar A, Mushtak A, Nahiduzzaman M. A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images. Diagnostics (Basel) 2023; 13:2537. [PMID: 37568900 PMCID: PMC10417300 DOI: 10.3390/diagnostics13152537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.
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Affiliation(s)
- Muntakim Mahmud Khan
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | | | - A. S. M. Shamsul Arefin
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Kanchon Kanti Podder
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Md. Sakib Abrar Hossain
- Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Abdulrahman Alqahtani
- Department of Medical Equipment Technology, College of Applied, Medical Science, Majmaah University, Majmaah City 11952, Saudi Arabia
- Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - M. Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India
- Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Perlis 02600, Malaysia
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha 3050, Qatar
| | - Md. Nahiduzzaman
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
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Mostafa Abd El-Aal El-Kady A, Mostafa M, Hamdy Ali Hussien H, Ali Moussa F. Comparative Analysis: Deep vs. Machine Learning for Early DFU Detection in Medical Imaging. 2023 INTELLIGENT METHODS, SYSTEMS, AND APPLICATIONS (IMSA) 2023. [DOI: 10.1109/imsa58542.2023.10217437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
| | - Mohamed Mostafa
- Beni-suef University,Faculty of Computers & Artificial Intelligence,Information Technology Dept.,Beni-suef,Egypt
| | - Heba Hamdy Ali Hussien
- Beni-suef University,Faculty of Computers & Artificial Intelligence,Assistant Professor Multimedia Dept.,Beni-suef,Egypt
| | - Farid Ali Moussa
- Beni-suef University,Faculty of Computers & Artificial Intelligence,Information Technology Dept.,Beni-suef,Egypt
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8
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Hernandez-Guedes A, Arteaga-Marrero N, Villa E, Callico GM, Ruiz-Alzola J. Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers. SENSORS (BASEL, SWITZERLAND) 2023; 23:757. [PMID: 36679552 PMCID: PMC9867159 DOI: 10.3390/s23020757] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.
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Affiliation(s)
- Abian Hernandez-Guedes
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
- Instituto Universitario de Microelectrónica Aplicada (IUMA), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Natalia Arteaga-Marrero
- Grupo Tecnología Médica IACTEC, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain
| | - Enrique Villa
- Grupo Tecnología Médica IACTEC, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain
| | - Gustavo M. Callico
- Instituto Universitario de Microelectrónica Aplicada (IUMA), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Juan Ruiz-Alzola
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
- Grupo Tecnología Médica IACTEC, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain
- Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
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Tehsin S, Kausar S, Jameel A. Diabetic wounds and artificial intelligence: A mini-review. World J Clin Cases 2023; 11:84-91. [PMID: 36687200 PMCID: PMC9846989 DOI: 10.12998/wjcc.v11.i1.84] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/12/2022] [Accepted: 12/23/2022] [Indexed: 01/04/2023] Open
Abstract
Diabetic wound takes longer time to heal due to micro and macro-vascular ailment. This longer healing time can lead to infections and other health complications. Foot ulcers are one of the most common diabetic wounds. These are one of the leading cause of amputations. Medical science is continuously striving for improving quality of human life. A recent trend of amalgamation of knowledge, efforts and technological advancement of medical science experts and artificial intelligence researchers, has made tremendous success in diagnosis, prognosis and treatment of a variety of diseases. Diabetic wounds are no exception, as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective manner. The presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science, for diabetic wound healing. Framework for diabetic wound assessment using artificial intelligence is presented. Moreover, this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound patients. The article also discusses the future directions for the betterment of the field that can lead to facilitate both, clinician and patients.
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Affiliation(s)
- Samabia Tehsin
- Computer Science, Bahria University, Karachi 75260, Sindh, Pakistan
| | - Sumaira Kausar
- Computer Science, Bahria University, Islamabad 46000, Pakistan
| | - Amina Jameel
- Department of Computer Engineering, Bahria University, Islamabad 46000, Pakistan
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Chemello G, Salvatori B, Morettini M, Tura A. Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review. BIOSENSORS 2022; 12:985. [PMID: 36354494 PMCID: PMC9688674 DOI: 10.3390/bios12110985] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/26/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.
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Affiliation(s)
- Gaetano Chemello
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
| | | | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
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11
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Kaselimi M, Protopapadakis E, Doulamis A, Doulamis N. A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring. Front Physiol 2022; 13:924546. [PMID: 36338484 PMCID: PMC9635839 DOI: 10.3389/fphys.2022.924546] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 10/05/2022] [Indexed: 06/04/2024] Open
Abstract
Diabetic foot complications have multiple adverse effects in a person's quality of life. Yet, efficient monitoring schemes can mitigate or postpone any disorders, mainly by early detecting regions of interest. Nowadays, optical sensors and artificial intelligence (AI) tools can contribute efficiently to such monitoring processes. In this work, we provide information on the adopted imaging schemes and related optical sensors on this topic. The analysis considers both the physiology of the patients and the characteristics of the sensors. Currently, there are multiple approaches considering both visible and infrared bands (multiple ranges), most of them coupled with various AI tools. The source of the data (sensor type) can support different monitoring strategies and imposes restrictions on the AI tools that should be used with. This review provides a comprehensive literature review of AI-assisted DFU monitoring methods. The paper presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and the challenges for transferring these methods into a practical and trustworthy framework for sufficient remote management of the patients.
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Affiliation(s)
- Maria Kaselimi
- National Technical University of Athens, School of Rural, Surveying and Geoinformatics Engineering, Athens, Greece
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Khandakar A, Mahmud S, Chowdhury MEH, Reaz MBI, Kiranyaz S, Mahbub ZB, Md Ali SH, Bakar AAA, Ayari MA, Alhatou M, Abdul-Moniem M, Faisal MAA. Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature. SENSORS (BASEL, SWITZERLAND) 2022; 22:7599. [PMID: 36236697 PMCID: PMC9572216 DOI: 10.3390/s22197599] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/01/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
An intelligent insole system may monitor the individual's foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals' foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.
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Affiliation(s)
- Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Zaid Bin Mahbub
- Department of Physics and Mathematics, North South University, Dhaka 1229, Bangladesh
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Mohamed Arselene Ayari
- Department of Civil and Architectural Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
- Technology Innovation and Engineering Education, College of Engineering, Qatar University, Doha 2713, Qatar
| | - Mohammed Alhatou
- Neuromuscular Division, Hamad General Hospital and Department of Neurology; Al Khor Hospital, Doha 3050, Qatar
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Islam KR, Kumar J, Tan TL, Reaz MBI, Rahman T, Khandakar A, Abbas T, Hossain MSA, Zughaier SM, Chowdhury MEH. Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning. Diagnostics (Basel) 2022; 12:diagnostics12092144. [PMID: 36140545 PMCID: PMC9498213 DOI: 10.3390/diagnostics12092144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/22/2022] [Accepted: 08/26/2022] [Indexed: 11/18/2022] Open
Abstract
With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.
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Affiliation(s)
- Khandaker Reajul Islam
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Correspondence: (J.K.); (M.E.H.C.)
| | - Toh Leong Tan
- Department of Emergency Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
| | - Tariq Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha P.O. Box 26999, Qatar
| | | | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
- Correspondence: (J.K.); (M.E.H.C.)
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DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion. SENSORS 2022; 22:s22145149. [PMID: 35890828 PMCID: PMC9318496 DOI: 10.3390/s22145149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/06/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022]
Abstract
Infrared images are robust against illumination variation and disguises, containing the sharp edge contours of objects. Visible images are enriched with texture details. Infrared and visible image fusion seeks to obtain high-quality images, keeping the advantages of source images. This paper proposes an object-aware image fusion method based on a deep residual shrinkage network, termed as DRSNFuse. DRSNFuse exploits residual shrinkage blocks for image fusion and introduces a deeper network in infrared and visible image fusion tasks than existing methods based on fully convolutional networks. The deeper network can effectively extract semantic information, while the residual shrinkage blocks maintain the texture information throughout the whole network. The residual shrinkage blocks adapt a channel-wise attention mechanism to the fusion task, enabling feature map channels to focus on objects and backgrounds separately. A novel image fusion loss function is proposed to obtain better fusion performance and suppress artifacts. DRSNFuse trained with the proposed loss function can generate fused images with fewer artifacts and more original textures, which also satisfy the human visual system. Experiments show that our method has better fusion results than mainstream methods through quantitative comparison and obtains fused images with brighter targets, sharper edge contours, richer details, and fewer artifacts.
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