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Khader A, Alquran H. Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images. Bioengineering (Basel) 2023; 10:764. [PMID: 37508791 PMCID: PMC10376879 DOI: 10.3390/bioengineering10070764] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/30/2023] Open
Abstract
Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.
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Affiliation(s)
- Ateka Khader
- Department of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
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Tiulpin A, Saarakkala S, Mathiessen A, Hammer HB, Furnes O, Nordsletten L, Englund M, Magnusson K. Predicting total knee arthroplasty from ultrasonography using machine learning. OSTEOARTHRITIS AND CARTILAGE OPEN 2022; 4:100319. [DOI: 10.1016/j.ocarto.2022.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/15/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022] Open
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Rytky SJO, Huang L, Tanska P, Tiulpin A, Panfilov E, Herzog W, Korhonen RK, Saarakkala S, Finnilä MAJ. Automated analysis of rabbit knee calcified cartilage morphology using micro-computed tomography and deep learning. J Anat 2021; 239:251-263. [PMID: 33782948 PMCID: PMC8273618 DOI: 10.1111/joa.13435] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/04/2021] [Accepted: 03/11/2021] [Indexed: 11/27/2022] Open
Abstract
Structural dynamics of calcified cartilage (CC) are poorly understood. Conventionally, CC structure is analyzed using histological sections. Micro‐computed tomography (µCT) allows for three‐dimensional (3D) imaging of mineralized tissues; however, the segmentation between bone and mineralized cartilage is challenging. Here, we present state‐of‐the‐art deep learning segmentation for µCT images to assess 3D CC morphology. The sample includes 16 knees from 12 New Zealand White rabbits dissected into osteochondral samples from six anatomical regions: lateral and medial femoral condyles, lateral and medial tibial plateaus, femoral groove, and patella (n = 96). The samples were imaged with µCT and processed for conventional histology. Manually segmented CC from the images was used to train segmentation models with different encoder–decoder architectures. The models with the greatest out‐of‐fold evaluation Dice score were selected. CC thickness was compared across 24 regions, co‐registered between the imaging modalities using Pearson correlation and Bland–Altman analyses. Finally, the anatomical CC thickness variation was assessed via a Linear Mixed Model analysis. The best segmentation models yielded average Dice of 0.891 and 0.807 for histology and µCT segmentation, respectively. The correlation between the co‐registered regions was strong (r = 0.897, bias = 21.9 µm, standard deviation = 21.5 µm). Finally, both methods could separate the CC thickness between the patella, femoral, and tibial regions (p < 0.001). As a conclusion, the proposed µCT analysis allows for ex vivo 3D assessment of CC morphology. We demonstrated the biomedical relevance of the method by quantifying CC thickness in different anatomical regions with a varying mean thickness. CC was thickest in the patella and thinnest in the tibial plateau. Our method is relatively straightforward to implement into standard µCT analysis pipelines, allowing the analysis of CC morphology. In future research, µCT imaging might be preferable to histology, especially when analyzing dynamic changes in cartilage mineralization. It could also provide further understanding of 3D morphological changes that may occur in mineralized cartilage, such as thickening of the subchondral plate in osteoarthritis and other joint diseases.
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Affiliation(s)
- Santeri J O Rytky
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Lingwei Huang
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Petri Tanska
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Aleksei Tiulpin
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.,Ailean Technologies Oy, Oulu, Finland
| | - Egor Panfilov
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Walter Herzog
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada
| | - Rami K Korhonen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Simo Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Mikko A J Finnilä
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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Power L, Acevedo L, Yamashita R, Rubin D, Martin I, Barbero A. Deep learning enables the automation of grading histological tissue engineered cartilage images for quality control standardization. Osteoarthritis Cartilage 2021; 29:433-443. [PMID: 33422705 DOI: 10.1016/j.joca.2020.12.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 12/22/2020] [Accepted: 12/28/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To automate the grading of histological images of engineered cartilage tissues using deep learning. METHODS Cartilaginous tissues were engineered from various cell sources. Safranin O and fast green stained histological images of the tissues were graded for chondrogenic quality according to the Modified Bern Score, which ranks images on a scale from zero to six according to the intensity of staining and cell morphology. The whole images were tiled, and the tiles were graded by two experts and grouped into four categories with the following grades: 0, 1-2, 3-4, and 5-6. Deep learning was used to train models to classify images into these histological score groups. Finally, the tile grades per donor were averaged. The root mean square errors (RMSEs) were calculated between each user and the model. RESULTS Transfer learning using a pretrained DenseNet model was selected. The RMSEs of the model predictions and 95% confidence intervals were 0.49 (0.37, 0.61) and 0.78 (0.57, 0.99) for each user, which was in the same range as the inter-user RMSE of 0.71 (0.51, 0.93). CONCLUSION Using supervised deep learning, we could automate the scoring of histological images of engineered cartilage and achieve results with errors comparable to inter-user error. Thus, the model could enable the automation and standardization of assessments currently used for experimental studies as well as release criteria that ensure the quality of manufactured clinical grafts and compliance with regulatory requirements.
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Affiliation(s)
- L Power
- Department of Biomedical Engineering, University of Basel, Switzerland; Department of Biomedicine, University Hospital Basel, University of Basel, Switzerland.
| | - L Acevedo
- Department of Biomedicine, University Hospital Basel, University of Basel, Switzerland.
| | - R Yamashita
- Department of Biomedical Data Science, Stanford University School of Medicine, USA.
| | - D Rubin
- Department of Biomedical Data Science, Stanford University School of Medicine, USA.
| | - I Martin
- Department of Biomedical Engineering, University of Basel, Switzerland; Department of Biomedicine, University Hospital Basel, University of Basel, Switzerland.
| | - A Barbero
- Department of Biomedicine, University Hospital Basel, University of Basel, Switzerland.
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Ferreira RG, Silva DDD, Elesbon AAA, Fernandes-Filho EI, Veloso GV, Fraga MDS, Ferreira LB. Machine learning models for streamflow regionalization in a tropical watershed. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 280:111713. [PMID: 33257181 DOI: 10.1016/j.jenvman.2020.111713] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/17/2020] [Accepted: 11/21/2020] [Indexed: 06/12/2023]
Abstract
This study aims to assess different machine learning approaches for streamflow regionalization in a tropical watershed, analyzing their advantages and limitations, and to point the benefits of using them for water resources management. The algorithms applied were: Random Forest, Earth and linear model. The response variables were the three types of minimum streamflow (Q7.10, Q95 and Q90), besides the long-term average streamflow (Qmld). The database involved 76 environmental covariates related to morphometry, topography, climate, land use and cover, and surface conditions. The elimination of covariates was performed using two processes: Pearson's correlation analysis and importance analysis by Recursive Feature Elimination (RFE). To validate the models, the following statistical metrics were used: Nash-Sutcliffe coefficient (NSE), percent bias (PBIAS), Willmott's index of agreement (d), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and relative error (RE). The linear model was unsatisfactory for all response variables. The results show that nonlinear models performed well, and their covariate of greatest predictive importance was flow equivalent to the precipitated volume, considering the subtraction of an abstraction factor of 750 mm (Peq750). Generally, the Random Forest and Earth models showed similar performances and great ability to predict the minimum streamflow and long-term average streamflow assessed, constituting powerful and promising alternatives for the streamflow regionalization in support to the management and integrated planning of water resources at the level of river basins.
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Affiliation(s)
- Renan Gon Ferreira
- Department of Agricultural Engineering, Federal University of Viçosa, Campus UFV, 36570-900, Viçosa, MG, Brazil.
| | - Demetrius David da Silva
- Department of Agricultural Engineering, Federal University of Viçosa, Campus UFV, 36570-900, Viçosa, MG, Brazil
| | | | | | - Gustavo Vieira Veloso
- Department of Soil and Plant Nutrition, Federal University of Viçosa, Campus UFV, 36570-900, Viçosa, MG, Brazil
| | | | - Lucas Borges Ferreira
- Department of Agricultural Engineering, Federal University of Viçosa, Campus UFV, 36570-900, Viçosa, MG, Brazil
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