Diabetic foot ulcer classification using mapped binary patterns and convolutional neural networks

Diabetic foot ulcer (DFU) is a major complication of diabetes and can lead to lower limb amputation if not treated early and properly. In addition to the traditional clinical approaches, in recent years, research on automation using computer vision and machine learning methods plays an important role in DFU classification, achieving promising successes. The most recent automatic approaches to DFU classification are based on convolutional neural networks (CNNs), using solely RGB images as input. In this paper, we present a CNN-based DFU classification method in which we showed that feeding an appropriate feature (texture information) to the CNN model provides a complementary performance to the standard RGB-based deep models of the DFU classification task, and better performance can be obtained if both RGB images and their texture features are combined and used as input to the CNN. To this end, the proposed method consists of two main stages. The first stage extracts texture information from the RGB image using the mapped binary patterns technique. The obtained mapped image is used to aid the second stage in recognizing DFU as it contains texture information of ulcer. The stack of RGB and mapped binary patterns images are fed to the CNN as a tensor input or as a fused image, which is a linear combination of RGB and mapped binary patterns images. The performance of the proposed approach was evaluated using two recently published DFU datasets: the Part-A dataset of healthy and unhealthy (DFU) cases [17] and Part-B dataset of ischaemia and infection cases [18]. The results showed that the proposed methods provided better performance than the state-of-the-art CNN-based methods with 0.981% (AUC) and 0.952% (F-Measure) on the Part-A dataset, 0.995% (AUC) and 0.990% (F-measure) for the Part-B ischaemia dataset, and 0.820% (AUC) and 0.744% (F-measure) on the Part-B infection dataset

Deriving alpha angle from anterior-posterior dual-energy x-ray absorptiometry scans : an automated and validated approach

Grant information: RE, MF, FS are supported by, and this work is funded by a Wellcome Trust collaborative award (209233). BGF is supported by a Medical Research Council (MRC) clinical research training fellowship (MR/S021280/1). BGF, MF, JHT, GDS work in the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the MRC (MC_UU_00011/1). CL was funded by the MRC, UK (MR/S00405X/1).Non peer reviewedPublisher PD

Cam morphology but neither acetabular dysplasia nor pincer morphology is associated with osteophytosis throughout the hip: findings from a cross-sectional study in UK Biobank

Objectives: to examine whether acetabular dysplasia (AD), cam and/or pincer morphology are associated with radiographic hip osteoarthritis (rHOA) and hip pain in UK Biobank (UKB) and, if so, what distribution of osteophytes is observed.Design: participants from UKB with a left hip dual-energy X-ray absorptiometry (DXA) scan had alpha angle (AA), lateral centre-edge angle (LCEA) and joint space narrowing (JSN) derived automatically. Cam and pincer morphology, and AD were defined using AA and LCEA. Osteophytes were measured manually and rHOA grades were calculated from JSN and osteophyte measures. Logistic regression was used to examine the relationships between these hip morphologies and rHOA, osteophytes, JSN, and hip pain.Results: 6,807 individuals were selected (mean age: 62.7; 3382/3425 males/females). Cam morphology was more prevalent in males than females (15.4% and 1.8% respectively). In males, cam morphology was associated with rHOA [OR 3.20 (95% CI 2.41–4.25)], JSN [1.53 (1.24–1.88)], and acetabular [1.87 (1.48–2.36)], superior [1.94 (1.45–2.57)] and inferior [4.75 (3.44–6.57)] femoral osteophytes, and hip pain [1.48 (1.05–2.09)]. Broadly similar associations were seen in females, but with weaker statistical evidence. Neither pincer morphology nor AD showed any associations with rHOA or hip pain.Conclusions: cam morphology was predominantly seen in males in whom it was associated with rHOA and hip pain. In males and females, cam morphology was associated with inferior femoral head osteophytes more strongly than those at the superior femoral head and acetabulum. Further studies are justified to characterise the biomechanical disturbances associated with cam morphology, underlying the observed osteophyte distribution

Machine-learning derived acetabular dysplasia and cam morphology are features of severe hip osteoarthritis : findings from UK Biobank

Acknowledgements and disclosures The authors would like to thank Dr Martin Williams, Consultant Musculoskeletal Radiologist North Bristol NHS Trust, who provided substantial training and expertise in osteophyte assessment on DXA images. This research has been conducted using the UK Biobank Resource (application number 17295). Financial Support: RE, MF, FS are supported, and this work is funded by a Wellcome Trust collaborative award (reference number 209233). BGF is supported by a Medical Research Council (MRC) clinical research training fellowship (MR/S021280/1). CL was funded by the MRC, UK (MR/S00405X/1) as well as a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (223267/Z/21/Z). NCH acknowledges support from the MRC and NIHR Southampton Biomedical Research Centre, University of Southampton, and University Hospital Southampton. This research was funded in whole, or in part, by the Wellcome Trust [Grant number 223267/Z/21/Z]. NCH has received consultancy, lecture fees and honoraria from Alliance for Better Bone Health, AMGEN, MSD, Eli Lilly, Servier, UCB, Shire, Consilient Healthcare, Kyowa Kirin and Internis Pharma. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPublisher PD

A Genome‐Wide Association Study Meta‐Analysis of Alpha Angle Suggests Cam‐Type Morphology May Be a Specific Feature of Hip Osteoarthritis in Older Adults

Objective: To examine the genetic architecture of cam morphology using alpha angle (AA) as a proxy measure and conduct an AA genome‐wide association study (GWAS) followed by Mendelian randomization (MR) to evaluate its causal relationship with hip osteoarthritis (OA). Methods: Observational analyses examined associations between AA measurements derived from hip dual x‐ray absorptiometry (DXA) scans from the UK Biobank study and radiographic hip OA outcomes and subsequent total hip replacement. Following these analyses, an AA GWAS meta‐analysis was performed (N = 44,214) using AA measurements previously derived in the Rotterdam Study. Linkage disequilibrium score regression assessed the genetic correlation between AA and hip OA. Genetic associations considered significant (P < 5 × 10−8) were used as AA genetic instrument for 2‐sample MR analysis. Results: DXA‐derived AA showed expected associations between AA and radiographic hip OA (adjusted odds ratio [OR] 1.63 [95% confidence interval (95% CI) 1.58, 1.67]) and between AA and total hip replacement (adjusted hazard ratio 1.45 [95% CI 1.33, 1.59]) in the UK Biobank study cohort. The heritability of AA was 10%, and AA had a moderate genetic correlation with hip OA (rg = 0.26 [95% CI 0.10, 0.43]). Eight independent genetic signals were associated with AA. Two‐sample MR provided weak evidence of causal effects of AA on hip OA risk (inverse variance weighted OR 1.84 [95% CI 1.14, 2.96], P = 0.01). In contrast, genetic predisposition for hip OA had stronger evidence of a causal effect on increased AA (inverse variance weighted β = 0.09 [95% CI 0.04, 0.13], P = 4.58 × 10−5). Conclusion: Expected observational associations between AA and related clinical outcomes provided face validity for the DXA‐derived AA measurements. Evidence of bidirectional associations between AA and hip OA, particularly for risk of hip OA on AA, suggests that hip shape modeling secondary to a genetic predisposition to hip OA contributes to the well‐established relationship between hip OA and cam morphology in older adults

Automatic segmentation of hip osteophytes in DXA scans sing U-nets

Osteophytes are distinctive radiographic features of osteo-arthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, this has not been possible due to the costs and challenges associated with manually outlining osteophytes in sufficiently large datasets. Automatic systems that can segment osteophytes would pave the way for this research and also have potential clinical applications. We propose, to the best of our knowledge, the first work on automating pixel-wise segmentation of osteophytes in hip dual-energy x-ray absorptiometry scans (DXAs). Based on U-Nets, we developed an automatic system to detect and segment osteophytes at the superior and the inferior femoral head, and the lateral acetabulum. The system achieved sensitivity, specificity, and average Dice scores (±std) of (0.98, 0.92, 0.71±0.19) for the superior femoral head [793 DXAs], (0.96, 0.85, 0.66±0.24) for the inferior femoral head [409 DXAs], and (0.94, 0.73, 0.64±0.24) for the lateral acetabulum [760 DXAs]. This work enables large-scale genetic analyses of the role of osteophytes in OA, and opens doors to using low-radiation DXAs for screening for radiographic hip OA