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Touahema S, Zaimi I, Zrira N, Ngote MN, Doulhousne H, Aouial M. MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis. Diagnostics (Basel) 2024; 14:993. [PMID: 38786291 PMCID: PMC11120168 DOI: 10.3390/diagnostics14100993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/20/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called "MedKnee" is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist's diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee's satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.
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Affiliation(s)
- Said Touahema
- MECAtronique Team, CPS2E Laboratory, Ecole Nationale Supérieure des Mines de Rabat, Rabat 10000, Morocco
- Ministry of Health and Social Protection, Provincial Ministerial Administration of El Kelaa des Sraghna, El Kelaa des Sraghna 43000, Morocco
| | - Imane Zaimi
- Multidisciplinary Research Laboratory for Science, Technology and Society, Department of Computer Engineering and Mathematics, Higher School of Technology, Khenifra, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
| | - Nabila Zrira
- ADOS Team, LISTD Laboratory, Ecole Nationale Supérieure des Mines de Rabat, Rabat 10000, Morocco
| | - Mohamed Nabil Ngote
- MECAtronique Team, CPS2E Laboratory, Ecole Nationale Supérieure des Mines de Rabat, Rabat 10000, Morocco
- Institut Supérieur d’Ingénierie et Technologies de Santé/Faculté de Médecine Abulcasis, Université Internationale Abulcasis des Sciences de la Santé, Rabat 10000, Morocco
| | - Hassan Doulhousne
- Avicenne Military Hospital, Faculty of Medicine and Pharmacy, Marrakech 40000, Morocco
| | - Mohsine Aouial
- Ministry of Health and Social Protection, Provincial Hospital Center of El Kelaa des Sraghna, El Kelaa des Sraghna 43000, Morocco
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2
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Pi SW, Lee BD, Lee MS, Lee HJ. Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images. Sci Rep 2023; 13:22887. [PMID: 38129653 PMCID: PMC10739741 DOI: 10.1038/s41598-023-50210-4] [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: 06/19/2023] [Accepted: 12/16/2023] [Indexed: 12/23/2023] Open
Abstract
The Kellgren-Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician's subjective assessment. Moreover, the accuracy varies significantly depending on the clinician's experience and can be particularly low. Therefore, in this study, we developed an ensemble network that can predict a consistent and accurate KL grade for knee osteoarthritis severity using a deep learning approach. We trained individual models on knee X-ray datasets using the most suitable image size for each model in an ensemble network rather than using datasets with a single image size. We then built the ensemble network using these models to overcome the instability of single models and further improve accuracy. We conducted various experiments using a dataset of 8260 images from the Osteoarthritis Initiative open dataset. The proposed ensemble network exhibited the best performance, achieving an accuracy of 76.93% and an F1-score of 0.7665. The Grad-CAM visualization technique was used to further evaluate the focus of the model. The results demonstrated that the proposed ensemble network outperforms existing techniques that have performed well in KL grade classification. Moreover, the proposed model focuses on the joint space around the knee to extract the imaging features required for KL grade classification, revealing its high potential for diagnosing knee osteoarthritis.
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Affiliation(s)
- Sun-Woo Pi
- Division of AI and Computer Engineering, Kyonggi University, Suwon, Republic of Korea
| | - Byoung-Dai Lee
- Division of AI and Computer Engineering, Kyonggi University, Suwon, Republic of Korea
| | - Mu Sook Lee
- Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea.
| | - Hae Jeong Lee
- Department of Pediatrics, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
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3
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Nooh MH, Alshehri MS, Alzahrani ZS, Alsolami HM, Almutairi AO, AlOtaibi AS, Aljohani AN. The Efficacy and Safety of Intra-articular Low Molecular Weight Fraction of Human Serum Albumin for the Management of Moderate to Moderately Severe Knee Osteoarthritis: A Systematic Review and Meta-Analysis. Cureus 2023; 15:e41240. [PMID: 37529519 PMCID: PMC10387823 DOI: 10.7759/cureus.41240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Osteoarthritis is a chronic degenerative joint disease that affects weight-bearing joints. Low molecular weight fraction of 5% (LMWF-5A) human serum albumin is an intra-articular injection that emerged for the treatment of knee osteoarthritis. The aim of this review is to assess the efficacy and safety of LMWF-5A versus placebo through a systematic review and meta-analysis. The Cochrane Central Register of Controlled Trials (CENTRAL), Medical Literature Analysis and Retrieval System Online (MEDLINE), EBSCO, and ClinicalTrials.gov registry databases were utilized to search for studies. Only randomized controlled trials (RCTs) that evaluated the efficacy of LMWF-5A versus placebo were included. Efficacy endpoints were represented by Western Ontario and McMaster Universities Arthritis Index (WOMAC) A and C scores for pain and function, respectively. Serious adverse events (SAEs), non-serious adverse events (NSAEs), and mortality rates were used to evaluate the safety of the drug. The revised Cochrane risk of bias tool was used for the risk of bias assessment. Seven RCTs (n=2939) that met the inclusion criteria were included. The meta-analysis did not find significant improvement in pain (WOMAC A) (standardized mean difference (SMD)= -0.01, 95% confidence interval (CI) -0.10 - 0.09, P=0.87, I²=30%). Additionally, no significant change in function was noted (WOMAC C) (SMD=0.01, 95% CI -0.08 - 0.10, P=0.87, I²=22%). The pooled analysis did not find a significant difference between LMWF-5A and placebo regarding the incidence of joint swelling (P=0.84), joint stiffness (P=0.53), arthralgia (P=0.53), extremity pain (P=0.45), NSAEs (P=0.21), SAEs (P=0.92), or mortality (P=1.00). However, the subgroup analysis showed a significant reduction of 42% in NSAEs upon administration of 10 mL of LMWF-5A (risk ratio (RR)=0.58, 95% CI 0.35-0.97, P=0.04). In summary, our meta-analysis did not find significant differences between LMWF-5A and placebo regarding the incidence of NSAEs, SAEs, or mortality. On the other hand, LMWF-5A did not demonstrate superiority over saline in terms of efficacy. Therefore, it is not an effective drug for managing knee osteoarthritis.
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Affiliation(s)
- Mohammad H Nooh
- Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU
- Medical Research, King Abdullah International Medical Research Center, Jeddah, SAU
| | - Mohammed S Alshehri
- Orthopaedic Surgery, King Abdulaziz Medical City, Jeddah, SAU
- Orthopaedic Surgery, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU
- Medical Research, King Abdullah International Medical Research Center, Jeddah, SAU
| | - Ziyad S Alzahrani
- Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU
- Medical Research, King Abdullah International Medical Research Center, Jeddah, SAU
| | - Hatem M Alsolami
- Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU
- Medical Research, King Abdullah International Medical Research Center, Jeddah, SAU
| | - Amal O Almutairi
- Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU
- Medical Research, King Abdullah International Medical Research Center, Jeddah, SAU
| | | | - Abdulaziz N Aljohani
- Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU
- Medical Research, King Abdullah International Medical Research Center, Jeddah, SAU
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4
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Mohammed AS, Hasanaath AA, Latif G, Bashar A. Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images. Diagnostics (Basel) 2023; 13:diagnostics13081380. [PMID: 37189481 DOI: 10.3390/diagnostics13081380] [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: 02/16/2023] [Revised: 03/19/2023] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren-Lawrence (KL) system. This requires the physician's expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.
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Affiliation(s)
- Abdul Sami Mohammed
- Computer Engineering Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Ahmed Abul Hasanaath
- Computer Science Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Ghazanfar Latif
- Computer Science Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Abul Bashar
- Computer Engineering Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
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5
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Chen N, Feng Z, Li F, Wang H, Yu R, Jiang J, Tang L, Rong P, Wang W. A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:164-170. [PMID: 36533422 DOI: 10.1039/d2ay01526a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOLO version 3 (YOLOv3) for automatic simultaneous localization of knee joints and quantification of radiographic knee OA. As an advanced deep convolutional neural network (CNN) algorithm for target detection, YOLOv3 enables simultaneous small object detection and quantification due to its unique residual connection and feature map merging. Hence, a unified CNN model is built for the elegant integration of knee joint detection and corresponding OA severity grading using the YOLOv3 framework. We achieve desirable accuracy in knee OA grading using the public and clinical datasets. It provides improvements in the precision, recall, F1 score and diagnostic accuracy of knee OA as well. Because of the fully automatic target detection and quantification, the time of handling an image is merely 40 ms from inputting the image to getting its label, supporting quick clinic decisions. It, thus, affords convenient and efficient image analysis for daily clinical diagnosis.
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Affiliation(s)
- Nan Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
| | - Zhichao Feng
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Fei Li
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
| | - Haibo Wang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
| | - Ruqin Yu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
| | - Jianhui Jiang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
| | - Lijuan Tang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha 410013, China
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6
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Yeoh PSQ, Lai KW, Goh SL, Hasikin K, Wu X, Li P. Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative. Front Bioeng Biotechnol 2023; 11:1164655. [PMID: 37122858 PMCID: PMC10136763 DOI: 10.3389/fbioe.2023.1164655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/24/2023] [Indexed: 05/02/2023] Open
Abstract
Knee osteoarthritis is one of the most common musculoskeletal diseases and is usually diagnosed with medical imaging techniques. Conventionally, case identification using plain radiography is practiced. However, we acknowledge that knee osteoarthritis is a 3D complexity; hence, magnetic resonance imaging will be the ideal modality to reveal the hidden osteoarthritis features from a three-dimensional view. In this work, the feasibility of well-known convolutional neural network (CNN) structures (ResNet, DenseNet, VGG, and AlexNet) to distinguish knees with and without osteoarthritis (OA) is investigated. Using 3D convolutional layers, we demonstrated the potential of 3D convolutional neural networks of 13 different architectures in knee osteoarthritis diagnosis. We used transfer learning by transforming 2D pre-trained weights into 3D as initial weights for the training of the 3D models. The performance of the models was compared and evaluated based on the performance metrics [balanced accuracy, precision, F1 score, and area under receiver operating characteristic (AUC) curve]. This study suggested that transfer learning indeed enhanced the performance of the models, especially for ResNet and DenseNet models. Transfer learning-based models presented promising results, with ResNet34 achieving the best overall accuracy of 0.875 and an F1 score of 0.871. The results also showed that shallow networks yielded better performance than deeper neural networks, demonstrated by ResNet18, DenseNet121, and VGG11 with AUC values of 0.945, 0.914, and 0.928, respectively. This encourages the application of clinical diagnostic aid for knee osteoarthritis using 3DCNN even in limited hardware conditions.
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Affiliation(s)
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Khin Wee Lai, ; Pei Li,
| | - Siew Li Goh
- Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Xiang Wu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Pei Li
- Information Department, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- *Correspondence: Khin Wee Lai, ; Pei Li,
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7
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Teoh YX, Lai KW, Usman J, Goh SL, Mohafez H, Hasikin K, Qian P, Jiang Y, Zhang Y, Dhanalakshmi S. Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4138666. [PMID: 35222885 PMCID: PMC8881170 DOI: 10.1155/2022/4138666] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 12/30/2022]
Abstract
Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.
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Affiliation(s)
- Yun Xin Teoh
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Juliana Usman
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Siew Li Goh
- Faculty of Medicine, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Hamidreza Mohafez
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Pengjiang Qian
- School of Artificial Intelligence and Computer Sciences, Jiangnan University, Wuxi 214122, China
| | - Yizhang Jiang
- School of Artificial Intelligence and Computer Sciences, Jiangnan University, Wuxi 214122, China
| | - Yuanpeng Zhang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong 226001, China
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
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8
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Zamzam AH, Al-Ani AKI, Wahab AKA, Lai KW, Satapathy SC, Khalil A, Azizan MM, Hasikin K. Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management. Front Public Health 2021; 9:782203. [PMID: 34869194 PMCID: PMC8637834 DOI: 10.3389/fpubh.2021.782203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 10/25/2021] [Indexed: 01/25/2023] Open
Abstract
The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.
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Affiliation(s)
- Aizat Hilmi Zamzam
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.,Engineering Services Department, Ministry of Health Malaysia, Putrajaya, Malaysia
| | | | - Ahmad Khairi Abdul Wahab
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | - Azira Khalil
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Malaysia
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
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9
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Guo X, Lei Y, He P, Zeng W, Yang R, Ma Y, Feng P, Lyu Q, Wang G, Shan H. An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT. Phys Med Biol 2021; 66. [PMID: 34715678 DOI: 10.1088/1361-6560/ac34b2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 10/29/2021] [Indexed: 12/16/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression,i.e.,from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, andF1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.
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Affiliation(s)
- Xiaodong Guo
- The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, People's Republic of China.,Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America
| | - Yiming Lei
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, People's Republic of China
| | - Peng He
- The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, People's Republic of China
| | - Wenbing Zeng
- Radiology Department, Chongqing University Three Gorges Hospital, Chongqing 404000, People's Republic of China
| | - Ran Yang
- Radiology Department, Chongqing University Three Gorges Hospital, Chongqing 404000, People's Republic of China
| | - Yinjin Ma
- The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, People's Republic of China
| | - Peng Feng
- The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, People's Republic of China
| | - Qing Lyu
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America
| | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America
| | - Hongming Shan
- Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, People's Republic of China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education), Fudan University, Shanghai 201210, People's Republic of China.,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201210, People's Republic of China
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10
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Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review. Int J Mol Sci 2021; 22:ijms22105235. [PMID: 34063436 PMCID: PMC8155859 DOI: 10.3390/ijms22105235] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/07/2021] [Accepted: 05/13/2021] [Indexed: 12/21/2022] Open
Abstract
Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.
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