Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Yeoh, Pauline Shan Qing; Lai, Khin Wee; Goh, Siew Li; Hasikin‬, Khairunnisa; Hum, Yan Chai; Tee, Yee Kai; Dhanalakshmi, Samiappan

doi:10.1155/2021/4931437

Cited by 70 publications

(43 citation statements)

References 72 publications

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“…e innovation of AI in the medical field is the creation of a smart approach to gather patient insights for automated disease detection and predictive analysis. AI solution has been heavily studied for OA diagnosis [37,38], and the outcomes are encouraging. Recently, OA prognosis has been an arising interest, which focuses on OA prevention.…”

Section: Machine Learning For Image-based Knee Oa Diagnosis and Progn...mentioning

confidence: 99%

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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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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Section: Machine Learning For Image-based Knee Oa Diagnosis and Progn...mentioning

confidence: 99%

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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“…CNNs are wired to capture the most important information in a sentence. They are effective due to their ability in mining the semantic clues in contextual windows, low complexity and are easy to train as the network learns throughout the optimization phases with a reduced number of parameters ( 49 , 61 ). Nonetheless, the limitation of CNNs is to preserve “sequential order and model long-distance contextual information.” To further matched such type of learning is the Recurrent models (RNN).…”

Section: Discussionmentioning

confidence: 99%

Predicting occupational injury causal factors using text-based analytics: A systematic review

Khairuddin

Hasikin‬

Razak

et al. 2022

Front. Public Health

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Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learningbased Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of , out of articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making

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“…The light propagated in the cladding region is measured for sensing the measure. The application of artificial intelligence improves the performance of the sensor in health care which makes it a promising future in sensor technology [47][48][49]. In this work, a novel apodization function is proposed to enhance the sensing characteristics and to maximize the usage of FBG as a vital sign sensor, which measures pulse as strain and temperature.…”

Section: Introductionmentioning

confidence: 99%

Novel Apodized Fiber Bragg Grating Applied for Medical Sensors: Performance Investigation

Arumugam¹,

Kumar²,

Dhanalakshmi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Sensors play an important role in shaping and monitoring human health. Exploration of methods to use Fiber Bragg Grating (FBG) with enhanced sensitivity has attracted great interest in the field of medical research. In this paper, a novel apodization function is proposed and performance evaluation and optimization of the same have been made. A comparison was conducted between various existing apodization functions and the proposed one based on optical characteristics and sensor parameters. The results evince the implementation of the proposed apodization function for vital sign measurement. The optical characteristics considered for evaluation are Peak Resonance Reflectivity level, Side Lobes Reflectivity level and Full Width Half Maximum (FWHM). The proposed novel apodization novel function has better FWHM, which is narrower than the FWHM of uniform FBG. Sensor characteristics like a quality parameter, detection accuracy and sensitivity also show improvement. The proposed novel apodization function is demonstrated to have a better shift in wavelength in terms of temperature and pulse measurement than the existing functions. The sensitivity of the proposed apodized function is enhanced with a Poly-dimethylsiloxane coating of varying thickness, which is 6 times and 5.14 times greater than uniform Fiber Bragg grating and FBG with the proposed novel apodization function, respectively, enhancing its utilization in the field of medicine.

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Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Cited by 70 publications

References 72 publications

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Predicting occupational injury causal factors using text-based analytics: A systematic review

Novel Apodized Fiber Bragg Grating Applied for Medical Sensors: Performance Investigation

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