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Life cycle assessment (LCA) of electrically-enhanced POME filtration: Environmental impacts of conductive-membrane formulation and process operating parameters

Ho KC, Teoh YX, Teow YH, Mohammad AW

J Environ Manage, 2021 Jan 01;277:111434.
PMID: 33045646 DOI: 10.1016/j.jenvman.2020.111434

This study assessed the environmental impacts of the formulation of graphene oxide (GO)/multi-walled carbon nanotubes (MWCNTs) conductive membranes and of the process operating parameters of electrically-enhanced palm oil mill effluent (POME) filtration. Two different analyses approaches were employed, cradle-to-gate approach for conductive membrane production and gate-to-gate approach for the POME filtration process. The parameters in conductive-membrane formulation (e.g. the weight ratio of carbon nanomaterials, and concentration of GO/MWCNT nanohybrids) and process operating parameters (e.g. electric field strength and electricity operating mode) were investigated. The findings herein are twofold. Firstly, for the fabrication of GO/MWCNT conductive membranes, the best weight ratio of GO:MWCNTs was found to be 1:9, given its superior membrane electrical conductivity with lower environmental impacts by 8.51% compared to pristine MWCNTs. The most suitable concentration of carbon nanomaterials was found to be 5 wt%, given its lowest impacts on resource depletion, human health, and ecosystems. Secondly, for the electrically-enhanced POME filtration, the optimum process operating parameters were found to be the application of an electric field of 300 V/cm in the continuous mode, given its lower environmental impacts (22.99%-89.30%) secondary to its requirement of the least electricity to produce permeate. The present study has established not only the optimized conditions in membrane formulation but also the operating parameters of electrically-enhanced filtration; such findings enable the use of cleaner production and sustainable approach to minimize fouling for industrial applications, whilst maintaining excellent efficiency.
Stratifying knee osteoarthritis features through multitask deep hybrid learning: Data from the osteoarthritis initiative

Teoh YX, Othmani A, Lai KW, Goh SL, Usman J

Comput Methods Programs Biomed, 2023 Dec;242:107807.
PMID: 37778138 DOI: 10.1016/j.cmpb.2023.107807

BACKGROUND AND OBJECTIVE: Knee osteoarthritis (OA) is a debilitating musculoskeletal disorder that causes functional disability. Automatic knee OA diagnosis has great potential of enabling timely and early intervention, that can potentially reverse the degenerative process of knee OA. Yet, it is a tedious task, concerning the heterogeneity of the disorder. Most of the proposed techniques demonstrated single OA diagnostic task widely based on Kellgren Lawrence (KL) standard, a composite score of only a few imaging features (i.e. osteophytes, joint space narrowing and subchondral bone changes). However, only one key disease pattern was tackled. The KL standard fails to represent disease pattern of individual OA features, particularly osteophytes, joint-space narrowing, and pain intensity that play a fundamental role in OA manifestation. In this study, we aim to develop a multitask model using convolutional neural network (CNN) feature extractors and machine learning classifiers to detect nine important OA features: KL grade, knee osteophytes (both knee, medial fibular: OSFM, medial tibial: OSTM, lateral fibular: OSFL, and lateral tibial: OSTL), joint-space narrowing (medial: JSM, and lateral: JSL), and patient-reported pain intensity from plain radiography.
METHODS: We proposed a new feature extraction method by replacing fully-connected layer with global average pooling (GAP) layer. A comparative analysis was conducted to compare the efficacy of 16 different convolutional neural network (CNN) feature extractors and three machine learning classifiers.
RESULTS: Experimental results revealed the potential of CNN feature extractors in conducting multitask diagnosis. Optimal model consisted of VGG16-GAP feature extractor and KNN classifier. This model not only outperformed the other tested models, it also outperformed the state-of-art methods with higher balanced accuracy, higher Cohen's kappa, higher F1, and lower mean squared error (MSE) in seven OA features prediction.
CONCLUSIONS: The proposed model demonstrates pain prediction on plain radiographs, as well as eight OA-related bony features. Future work should focus on exploring additional potential radiological manifestations of OA and their relation to therapeutic interventions.
A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects

Teoh YX, Alwan JK, Shah DS, Teh YW, Goh SL

Clin Biomech (Bristol, Avon), 2024 Mar;113:106188.
PMID: 38350282 DOI: 10.1016/j.clinbiomech.2024.106188

BACKGROUND: Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains.
METHODS: Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used.
FINDINGS: Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies.
INTERPRETATIONS: The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.
Fulltext Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh YX, Lai KW, Usman J, Goh SL, Mohafez H, Hasikin K, et al.

J Healthc Eng, 2022;2022:4138666.
PMID: 35222885 DOI: 10.1155/2022/4138666

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.