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General General

Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation.

In Therapeutic advances in musculoskeletal disease

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.

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

2021

Osteoporosis, QCT, artificial intelligence, computed tomography, epidemiology, fragility fracture, innovation, screening, technology, vertebral fracture

General General

A Multitask Approach to Learn Molecular Properties.

In Journal of chemical information and modeling

The endeavors to pursue a robust multitask model to resolve intertask correlations have lasted for many years. A multitask deep neural network, as the most widely used multitask framework, however, experiences several issues such as inconsistent performance improvement over the independent model benchmark. The research aims to introduce an alternative framework by using the problem transformation methods. We build our multitask models essentially based on the stacking of a base regressor and classifier, where the multitarget predictions are realized from an additional training stage on the expanded molecular feature space. The model architecture is implemented on the QM9, Alchemy, and Tox21 datasets, by using a variety of baseline machine learning techniques. The resultant multitask performance shows 1 to 10% enhancement of forecasting precision, with the task prediction accuracy being consistently improved over the independent single-target models. The proposed method demonstrates a notable superiority in tackling the intertarget dependence and, moreover, a great potential to simulate a wide range of molecular properties under the transformation framework.

Tan Zheng, Li Yan, Shi Weimei, Yang Shiqing

2021-Jul-21

Public Health Public Health

Metabolomic analyses reveals new stage-specific features of the COVID-19.

In The European respiratory journal

The current pandemic of coronavirus disease 19 (COVID-19) has affected more than 160 million of individuals and caused millions of deaths worldwide at least in part due to the unclarified pathophysiology of this disease. Therefore, identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual by acquiring extensive insights into the pathophysiological significance during disease progression. We provide a comprehensive view of metabolic characterization of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both discovery cohort and validation cohort, which suggest that metabolic reprogramming of glucose metabolism and urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and urea cycle may be a viable approach to fight against COVID-19 at various stages along the disease course.

Jia Hongling, Liu Chaowu, Li Dantong, Huang Qingsheng, Liu Dong, Zhang Ying, Ye Chang, Zhou Di, Wang Yang, Tan Yanlian, Li Kuibiao, Lin Fangqin, Zhang Haiqing, Lin Jingchao, Xu Yang, Liu Jingwen, Zeng Qing, Hong Jian, Chen Guobing, Zhang Hao, Zheng Lingling, Deng Xilong, Ke Changwen, Gao Yunfei, Fan Jun, Di Biao, Liang Huiying

2021-Jul-21

General General

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset.

In Frontiers in physiology

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.

Sánchez Jorge, Luongo Giorgio, Nothstein Mark, Unger Laura A, Saiz Javier, Trenor Beatriz, Luik Armin, Dössel Olaf, Loewe Axel

2021

atrial fibrillation, bidomain, cardiac modeling, density, fibrosis, machine learning, transmurality

General General

HADLN: Hybrid Attention-Based Deep Learning Network for Automated Arrhythmia Classification.

In Frontiers in physiology

In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In order to solve the problems of ignoring the correlation between contexts and gradient dispersion in traditional deep convolution neural network model, the hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification. The HADLN can make full use of the advantages of residual network (ResNet) and bidirectional long-short-term memory (Bi-LSTM) architecture to obtain fusion features containing local and global information and improve the interpretability of the model through the attention mechanism. The method is trained and verified by using the PhysioNet 2017 challenge dataset. Without loss of generality, the ECG signal is classified into four categories, including atrial fibrillation, noise, other, and normal signals. By combining the fusion features and the attention mechanism, the learned model has a great improvement in classification performance and certain interpretability. The experimental results show that the proposed HADLN method can achieve precision of 0.866, recall of 0.859, accuracy of 0.867, and F1-score of 0.880 on 10-fold cross-validation.

Jiang Mingfeng, Gu Jiayan, Li Yang, Wei Bo, Zhang Jucheng, Wang Zhikang, Xia Ling

2021

ResNet, arrhythmia classification, attention mechanism, bidirectional LSTM, deep learning

General General

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices.

In Frontiers in computational neuroscience

Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate inference of natural language processing applications. We demonstrate a path to software-equivalent accuracy for the GLUE benchmark on BERT (Bidirectional Encoder Representations from Transformers), by combining noise-aware training to combat inherent PCM drift and noise sources, together with reduced-precision digital attention-block computation down to INT6.

Spoon Katie, Tsai Hsinyu, Chen An, Rasch Malte J, Ambrogio Stefano, Mackin Charles, Fasoli Andrea, Friz Alexander M, Narayanan Pritish, Stanisavljevic Milos, Burr Geoffrey W

2021

BERT, DNN, PCM, RRAM, Transformer, analog accelerators, in-memory computing