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Article ; Online: Computer-Aided Diagnostic Systems for Osteoporotic Vertebral Fracture Detection: Opportunities and Challenges.

Bromiley, Paul A / Clark, Emma M / Poole, Kenneth E

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research

2020 Volume 35, Issue 12, Page(s) 2305–2306

MeSH term(s)	Computers ; Humans ; Osteoporotic Fractures/diagnostic imaging ; Spinal Fractures/diagnostic imaging ; Spine ; Tomography, X-Ray Computed
Language	English
Publishing date	2020-11-14
Publishing country	United States
Document type	Editorial ; Comment
ZDB-ID	632783-7
ISSN	1523-4681 ; 0884-0431
ISSN (online)	1523-4681
ISSN	0884-0431
DOI	10.1002/jbmr.4205
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Article ; Online: Understanding current UK practice for the incidental identification of vertebral fragility fractures from CT scans: an expert elicitation study.

Dalal, Garima / Bromiley, Paul A / Kariki, Eleni P / Luetchens, Shawn / Cootes, Timothy F / Payne, Katherine

Aging clinical and experimental research

2022 Volume 34, Issue 8, Page(s) 1909–1918

Abstract: Background: There is an emerging interest in using automated approaches to enable the incidental identification of vertebral fragility fractures (VFFs) on existing medical images visualising the spine.: Aim: To quantify values, and the degree of ... ...

Abstract	Background: There is an emerging interest in using automated approaches to enable the incidental identification of vertebral fragility fractures (VFFs) on existing medical images visualising the spine. Aim: To quantify values, and the degree of uncertainty associated with them, for the incidental identification of VFFs from computed tomography (CT) scans in current practice. Methods: An expert elicitation exercise was conducted to generate point estimates and measures of uncertainty for four values representing the probability of: VFF being correctly reported by the radiologist; the absence of VFF being correctly assessed by the radiologist; being referred for management when a VFF is identified; having a dual-energy X-ray absorptiometry (DXA) scan after general practitioner (GP) referral. Data from a sample of seven experts in the diagnosis and management of people with VFFs were pooled using mathematical aggregation. Results: The estimated mean values for each probability parameter were: VFF being correctly reported by the radiologist = 0.25 (standard deviation (SD): 0.21); absence of VFF being correctly assessed by the radiologist = 0.89 (0.10); being referred for management when a VFF is identified by the radiologist = 0.15 (0.12); having a DXA scan after GP referral = 0.66 (0.28). Discussion: These estimates could be used to facilitate the subsequent early economic evaluation of potential new approaches to improve the health outcomes of people with VFFs. Conclusion: In the absence of epidemiological studies, this study produced point estimates and measures of uncertainty for key parameters needed to describe current pathways for the incidental diagnosis of VFFs.
MeSH term(s)	Absorptiometry, Photon/methods ; Bone Density ; Humans ; Osteoporosis/complications ; Osteoporosis/diagnostic imaging ; Osteoporotic Fractures ; Spinal Fractures/complications ; Spinal Fractures/diagnostic imaging ; Tomography, X-Ray Computed/methods ; United Kingdom
Language	English
Publishing date	2022-04-18
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2104785-6
ISSN	1720-8319 ; 1594-0667
ISSN (online)	1720-8319
ISSN	1594-0667
DOI	10.1007/s40520-022-02124-w
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Zs.A 3343: Show issues

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Article ; Online: Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment.

Wilson, Anthony / Saeed, Haroon / Pringle, Catherine / Eleftheriou, Iliada / Bromiley, Paul A / Brass, Andy

BMJ health & care informatics

2021 Volume 28, Issue 1

Abstract: There is much discussion concerning 'digital transformation' in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to ... ...

Abstract	There is much discussion concerning 'digital transformation' in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use.
MeSH term(s)	Artificial Intelligence ; Data Management ; Delivery of Health Care/methods ; Humans
Language	English
Publishing date	2021-07-27
Publishing country	England
Document type	Journal Article ; Review
ISSN	2632-1009
ISSN (online)	2632-1009
DOI	10.1136/bmjhci-2021-100323
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Article ; Online: Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks.

McSweeney, Dónal M / Henderson, Edward G / van Herk, Marcel / Weaver, Jamie / Bromiley, Paul A / Green, Andrew / McWilliam, Alan

Medical physics

2022 Volume 49, Issue 5, Page(s) 3107–3120

Abstract: Background: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto-segmentation models.: Purpose: There ...

Abstract	Background: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto-segmentation models. Purpose: There is a need to define methodologies for applying models to different domains (e.g., anatomical regions or imaging modalities) without dramatically increasing data annotation. Methods: To address this problem, we empirically evaluate the generalizability of various source tasks for transfer learning: natural image classification, natural image segmentation, unsupervised image reconstruction, and self-supervised jigsaw solving. Axial CT slices at L3 were extracted from PET-CT scans for 204 oesophago-gastric cancer patients and the skeletal muscle manually delineated by an expert. Features were transferred and segmentation models trained on subsets ( Results: We find that accurate segmentation models can be trained on a fraction of the data required by current approaches. The Dice similarity coefficient and root mean square distance-to-agreement were calculated for each prediction and used to assess model performance. Models pre-trained on a segmentation task and fine-tuned on 10 images produce delineations that are comparable to those from trained observers and extract reliable measures of muscle health. Conclusions: Appropriate transfer learning can generate convolutional neural networks for abdominal muscle segmentation that achieve human-level performance while decreasing the required data by an order of magnitude, compared to previous methods (
MeSH term(s)	Abdominal Muscles ; Humans ; Image Processing, Computer-Assisted/methods ; Machine Learning ; Neural Networks, Computer ; Positron Emission Tomography Computed Tomography
Language	English
Publishing date	2022-02-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	188780-4
ISSN	2473-4209 ; 0094-2405
ISSN (online)	2473-4209
ISSN	0094-2405
DOI	10.1002/mp.15533
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Article ; Online: Educating the Healthcare Workforce to Support Digital Transformation.

Davies, Angela C / Davies, Alan / Abdulhussein, Hatim / Hooley, Frances / Eleftheriou, Iliada / Hassan, Lamiece / Bromiley, Paul A / Couch, Philip / Wasiuk, Catherine / Brass, Andy

Studies in health technology and informatics

2022 Volume 290, Page(s) 934–936

Abstract: Digital transformation of the healthcare workforce is a priority if we are to leverage the potential of digital technologies, artificial intelligence in clinical decision support and the potential of data captured within electronic health records. ... ...

Abstract	Digital transformation of the healthcare workforce is a priority if we are to leverage the potential of digital technologies, artificial intelligence in clinical decision support and the potential of data captured within electronic health records. Educational programmes need to be diverse and support the digital novices through to the champions whom will be responsible for procuring and implementing digital solutions. In order to professionalise the workforce in this area, digital competencies need to be built into training from early on and be underpinned by frameworks that help to guide regulators and professional bodies and support educational providers to deliver them. Here we describe Manchester's involvement in the development of digital competency frameworks and our digital transformation education programmes that we have created, including a Massive Online Open Course and a professional development course for England's Topol Digital Fellows.
MeSH term(s)	Artificial Intelligence ; Delivery of Health Care ; Health Personnel/education ; Humans ; Workforce
Language	English
Publishing date	2022-06-08
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI220217
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Article ; Online: Diagnostic performance of morphometric vertebral fracture analysis (MXA) in children using a 33-point software program.

Alqahtani, Fawaz F / Crabtree, Nicola J / Bromiley, Paul A / Cootes, Timothy / Broadley, Penny / Lang, Isla / Offiah, Amaka C

Bone

2020 Volume 133, Page(s) 115249

Abstract: Background: There is significant inter and intraobserver variability in diagnosing vertebral fractures in children.: Purpose: We aimed to evaluate the diagnostic accuracy of morphometric vertebral fracture analysis (MXA) using a 33-point software ... ...

Abstract	Background: There is significant inter and intraobserver variability in diagnosing vertebral fractures in children. Purpose: We aimed to evaluate the diagnostic accuracy of morphometric vertebral fracture analysis (MXA) using a 33-point software program designed for adults, on dual-energy x-ray absorptiometry (DXA) images of children. Materials and methods: Lateral spine DXA images of 420 children aged between 5 and 18 years were retrospectively reviewed. Vertebral fracture assessment (VFA) by an expert pediatric radiologist using Genant's semiquantitative scoring system served as the gold standard. All 420 DXA scans were analyzed by a trained radiographer, using semi-automated software (33-point morphometry). VFA of a random sample of 100 DXA was performed by an experienced pediatric clinical scientist. MXA of a random sample of 30 DXA images were analyzed by three pediatric radiologists and the pediatric clinical scientist. Diagnostic accuracy and inter and intraobserver agreement (kappa statistics) were calculated. Results: Overall sensitivity, specificity, false positive (FP) and false negative (FN) rates for the radiographer using the MXA software were 80%, 90%, 10%, and 20% respectively and for mild fractures alone were 46%, 92%, 8%, and 54% respectively. Overall sensitivity, specificity, FP, and FN rates for the four additional observers using MXA were 89%, 79%, 21%, and 11% respectively and for mild fractures alone were 36%, 86%, 14%, and 64% respectively. Agreement between two expert observers was fair to good for VFA and MXA [kappa = 0·29 to 0·76 (95% CI: 0·17-0·88) and 0·29 to 0·69 (95% CI: 0·17-0·83)] respectively. Conclusion: MXA using a 33-point technique developed for adults is not a reliable method for the identification of mild vertebral fractures in children. A pediatric standard is required which not only incorporates specific vertebral body height ratios but also the age-related physiological changes in vertebral shape that occur throughout childhood.
MeSH term(s)	Absorptiometry, Photon ; Adolescent ; Adult ; Child ; Child, Preschool ; Humans ; Retrospective Studies ; Software ; Spinal Fractures/diagnostic imaging ; Spine
Language	English
Publishing date	2020-01-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	632515-4
ISSN	1873-2763 ; 8756-3282
ISSN (online)	1873-2763
ISSN	8756-3282
DOI	10.1016/j.bone.2020.115249
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Zs.A 1571: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article: Digital health interventions for osteoporosis and post-fragility fracture care.

Gupta, Amit / Maslen, Christina / Vindlacheruvu, Madhavi / Abel, Richard L / Bhattacharya, Pinaki / Bromiley, Paul A / Clark, Emma M / Compston, Juliet E / Crabtree, Nicola / Gregory, Jennifer S / Kariki, Eleni P / Harvey, Nicholas C / McCloskey, Eugene / Ward, Kate A / Poole, Kenneth E S

Therapeutic advances in musculoskeletal disease

2022 Volume 14, Page(s) 1759720X221083523

Abstract: The growing burden from osteoporosis and fragility fractures highlights a need to improve osteoporosis management across healthcare systems. Sub-optimal management of osteoporosis is an area suitable for digital health interventions. While fracture ... ...

Abstract	The growing burden from osteoporosis and fragility fractures highlights a need to improve osteoporosis management across healthcare systems. Sub-optimal management of osteoporosis is an area suitable for digital health interventions. While fracture liaison services (FLSs) are proven to greatly improve care for people with osteoporosis, such services might benefit from technologies that enhance automation. The term 'Digital Health' covers a variety of different tools including clinical decision support systems, electronic medical record tools, patient decision aids, patient apps, education tools, and novel artificial intelligence (AI) algorithms. Within the scope of this review are AI solutions that use algorithms within health system registries to target interventions. Clinician-targeted, patient-targeted, or system-targeted digital health interventions could be used to improve management and prevent fragility fractures. This review was commissioned by The Royal Osteoporosis Society and Bone Research Academy during the production of the 2020 Research Roadmap (https://theros.org.uk), with the intention of identifying gaps where targeted research funding could lead to improved patient health. We explore potential uses of digital technology in the general management of osteoporosis. Evidence suggests that digital technologies can support multidisciplinary teams to provide the best possible patient care based on current evidence and to support patients in self-management. However, robust randomised controlled studies are still needed to assess the effectiveness and cost-effectiveness of these technologies.
Language	English
Publishing date	2022-03-28
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2516075-8
ISSN	1759-7218 ; 1759-720X
ISSN (online)	1759-7218
ISSN	1759-720X
DOI	10.1177/1759720X221083523
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Article ; Online: Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting.

Lindner, Claudia / Bromiley, Paul A / Ionita, Mircea C / Cootes, Tim F

IEEE transactions on pattern analysis and machine intelligence

2015 Volume 37, Issue 9, Page(s) 1862–1874

Abstract: A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to ... ...

Abstract	A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.
MeSH term(s)	Face/anatomy & histology ; Humans ; Image Processing, Computer-Assisted/methods ; Models, Statistical ; Pattern Recognition, Automated/methods ; Regression Analysis
Language	English
Publishing date	2015-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2014.2382106
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Article: Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation.

Aggarwal, Veena / Maslen, Christina / Abel, Richard L / Bhattacharya, Pinaki / Bromiley, Paul A / Clark, Emma M / Compston, Juliet E / Crabtree, Nicola / Gregory, Jennifer S / Kariki, Eleni P / Harvey, Nicholas C / Ward, Kate A / Poole, Kenneth E S

Therapeutic advances in musculoskeletal disease

2021 Volume 13, Page(s) 1759720X211024029

Abstract: Osteoporosis causes bones to become weak, porous and fracture more easily. While a vertebral fracture is the archetypal fracture of osteoporosis, it is also the most difficult to diagnose clinically. Patients often suffer further spine or other fractures, ...

Abstract	Osteoporosis causes bones to become weak, porous and fracture more easily. While a vertebral fracture is the archetypal fracture of osteoporosis, it is also the most difficult to diagnose clinically. Patients often suffer further spine or other fractures, deformity, height loss and pain before diagnosis. There were an estimated 520,000 fragility fractures in the United Kingdom (UK) in 2017 (costing £4.5 billion), a figure set to increase 30% by 2030. One way to improve both vertebral fracture identification and the diagnosis of osteoporosis is to assess a patient's spine or hips during routine computed tomography (CT) scans. Patients attend routine CT for diagnosis and monitoring of various medical conditions, but the skeleton can be overlooked as radiologists concentrate on the primary reason for scanning. More than half a million CT scans done each year in the National Health Service (NHS) could potentially be screened for osteoporosis (increasing 5% annually). If CT-based screening became embedded in practice, then the technique could have a positive clinical impact in the identification of fragility fracture and/or low bone density. Several companies have developed software methods to diagnose osteoporosis/fragile bone strength and/or identify vertebral fractures in CT datasets, using various methods that include image processing, computational modelling, artificial intelligence and biomechanical engineering concepts. Technology to evaluate Hounsfield units is used to calculate bone density, but not necessarily bone strength. In this rapid evidence review, we summarise the current literature underpinning approved technologies for opportunistic screening of routine CT images to identify fractures, bone density or strength information. We highlight how other new software technologies have become embedded in NHS clinical practice (having overcome barriers to implementation) and highlight how the novel osteoporosis technologies could follow suit. We define the key unanswered questions where further research is needed to enable the adoption of these technologies for maximal patient benefit.
Language	English
Publishing date	2021-07-10
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2516075-8
ISSN	1759-7218 ; 1759-720X
ISSN (online)	1759-7218
ISSN	1759-720X
DOI	10.1177/1759720X211024029
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Article ; Online: TINA manual landmarking tool: software for the precise digitization of 3D landmarks.

Schunke, Anja C / Bromiley, Paul A / Tautz, Diethard / Thacker, Neil A

Frontiers in zoology

2012 Volume 9, Issue 1, Page(s) 6

Abstract: Background: Interest in the placing of landmarks and subsequent morphometric analyses of shape for 3D data has increased with the increasing accessibility of computed tomography (CT) scanners. However, current computer programs for this task suffer from ...

Abstract	Background: Interest in the placing of landmarks and subsequent morphometric analyses of shape for 3D data has increased with the increasing accessibility of computed tomography (CT) scanners. However, current computer programs for this task suffer from various practical drawbacks. We present here a free software tool that overcomes many of these problems. Results: The TINA Manual Landmarking Tool was developed for the digitization of 3D data sets. It enables the generation of a modifiable 3D volume rendering display plus matching orthogonal 2D cross-sections from DICOM files. The object can be rotated and axes defined and fixed. Predefined lists of landmarks can be loaded and the landmarks identified within any of the representations. Output files are stored in various established formats, depending on the preferred evaluation software. Conclusions: The software tool presented here provides several options facilitating the placing of landmarks on 3D objects, including volume rendering from DICOM files, definition and fixation of meaningful axes, easy import, placement, control, and export of landmarks, and handling of large datasets. The TINA Manual Landmark Tool runs under Linux and can be obtained for free from http://www.tina-vision.net/tarballs/.
Language	English
Publishing date	2012-04-05
Publishing country	England
Document type	Journal Article
ISSN	1742-9994
ISSN (online)	1742-9994
DOI	10.1186/1742-9994-9-6
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