Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations

Background: Cardiac function quantification in cardiovascular magnetic resonance requires precise contouring of the heart chambers. This time-consuming task is increasingly being addressed by a plethora of ever more complex deep learning methods. However, only a small fraction of these have made their way from academia into clinical practice. In the quality assessment and control of medical artificial intelligence, the opaque reasoning and associated distinctive errors of neural networks meet an extraordinarily low tolerance for failure. Aim: The aim of this study is a multilevel analysis and comparison of the performance of three popular convolutional neural network (CNN) models for cardiac function quantification. Methods: U-Net, FCN, and MultiResUNet were trained for the segmentation of the left and right ventricles on short-axis cine images of 119 patients from clinical routine. The training pipeline and hyperparameters were kept constant to isolate the influence of network architecture. CNN performance was evaluated against expert segmentations for 29 test cases on contour level and in terms of quantitative clinical parameters. Multilevel analysis included breakdown of results by slice position, as well as visualization of segmentation deviations and linkage of volume differences to segmentation metrics via correlation plots for qualitative analysis. Results: All models showed strong correlation to the expert with respect to quantitative clinical parameters (r(z)(') = 0.978, 0.977, 0.978 for U-Net, FCN, MultiResUNet respectively). The MultiResUNet significantly underestimated ventricular volumes and left ventricular myocardial mass. Segmentation difficulties and failures clustered in basal and apical slices for all CNNs, with the largest volume differences in the basal slices (mean absolute error per slice: 4.2 +/- 4.5 ml for basal, 0.9 +/- 1.3 ml for midventricular, 0.9 +/- 0.9 ml for apical slices). Results for the right ventricle had higher variance and more outliers compared to the left ventricle. Intraclass correlation for clinical parameters was excellent (>= 0.91) among the CNNs. Conclusion: Modifications to CNN architecture were not critical to the quality of error for our dataset. Despite good overall agreement with the expert, errors accumulated in basal and apical slices for all models

Identification of a non-purple tartrate-resistant acid phosphatase: an evolutionary link to Ser/Thr protein phosphatases?

BACKGROUND Tartrate-resistant acid phosphatases (TRAcPs), also known as purple acid phosphatases (PAPs), are a family of binuclear metallohydrolases that have been identified in plants, animals and fungi. The human enzyme is a major histochemical marker for the diagnosis of bone-related diseases. TRAcPs can occur as a small form possessing only the ~35 kDa catalytic domain, or a larger ~55 kDa form possessing both a catalytic domain and an additional N-terminal domain of unknown function. Due to its role in bone resorption the 35 kDa TRAcP has become a promising target for the development of anti-osteoporotic chemotherapeutics. FINDINGS A new human gene product encoding a metallohydrolase distantly related to the ~55 kDa plant TRAcP was identified and characterised. The gene product is found in a number of animal species, and is present in all tissues sampled by the RIKEN mouse transcriptome project. Construction of a homology model illustrated that six of the seven metal-coordinating ligands in the active site are identical to that observed in the TRAcP family. However, the tyrosine ligand associated with the charge transfer transition and purple color of TRAcPs is replaced by a histidine. CONCLUSION The gene product identified here may represent an evolutionary link between TRAcPs and Ser/Thr protein phosphatases. Its biological function is currently unknown but is unlikely to be associated with bone metabolism.This work was funded by the Royal Society of Tropical Medicine and Hygiene through a Dennis Burkitt Fellowship to JJM. ARD is supported by the Economic and Social Research Council. JJM is supported by a Wellcome Trust Research Training Fellowship (GR074833MA)

Success Factors of European Syndromic Surveillance Systems: A Worked Example of Applying Qualitative Comparative Analysis

Introduction: Syndromic surveillance aims at augmenting traditional public health surveillance with timely information. To gain a head start, it mainly analyses existing data such as from web searches or patient records. Despite the setup of many syndromic surveillance systems, there is still much doubt about the benefit of the approach. There are diverse interactions between performance indicators such as timeliness and various system characteristics. This makes the performance assessment of syndromic surveillance systems a complex endeavour. We assessed if the comparison of several syndromic surveillance systems through Qualitative Comparative Analysis helps to evaluate performance and identify key success factors. Materials and Methods: We compiled case-based, mixed data on performance and characteristics of 19 syndromic surveillance systems in Europe from scientific and grey literature and from site visits. We identified success factors by applying crisp-set Qualitative Comparative Analysis. We focused on two main areas of syndromic surveillance application: seasonal influenza surveillance and situational awareness during different types of potentially health threatening events. Results: We found that syndromic surveillance systems might detect the onset or peak of seasonal influenza earlier if they analyse non-clinical data sources. Timely situational awareness during different types of events is supported by an automated syndromic surveillance system capable of analysing multiple syndromes. To our surprise, the analysis of multiple data sources was no key success factor for situational awareness. Conclusions: We suggest to consider these key success factors when designing or further developing syndromic surveillance systems. Qualitative Comparative Analysis helped interpreting complex, mixed data on small-N cases and resulted in concrete and practically relevant findings

The Knee Clinical Assessment Study – CAS(K). A prospective study of knee pain and knee osteoarthritis in the general population

BACKGROUND: Knee pain affects an estimated 25% of the adult population aged 50 years and over. Osteoarthritis is the most common diagnosis made in older adults consulting with knee pain in primary care. However, the relationship between this diagnosis and both the current disease-based definition of osteoarthritis and the regional pain syndrome of knee pain and disability is unclear. Expert consensus, based on current evidence, views the disease and the syndrome as distinct entities but the clinical usefulness of these two approaches to classifying knee pain in older adults has not been established. We plan to conduct a prospective, population-based, observational cohort study to investigate the relative merits of disease-based and regional pain syndrome-based approaches to classification and prognosis of knee pain in older adults. METHODS: All patients aged 50 years and over registered with three general practices in North Staffordshire will be invited to take part in a two-stage postal survey. Respondents to this survey phase who indicate that they have experienced knee pain within the previous 12 months will be invited to attend a research clinic for a detailed assessment. This will consist of clinical interview, physical examination, digital photography, plain x-rays, anthropometric measurement and a brief self-complete questionnaire. All consenting clinic attenders will be followed up by (i) general practice medical record review, (ii) repeat postal questionnaire at 18-months

Two-Year Progress of Pilot Research Activities in Teaching Digital Thinking Project (TDT)

This article presents a progress report from the last two years of the Teaching Digital Thinking (TDT) project. This project aims to implement new concepts, didactic methods, and teaching formats for sustainable digital transformation in Austrian Universities’ curricula by introducing new digital competencies. By equipping students and teachers with 21st-century digital competencies, partner universities can contribute to solving global challenges and organizing pilot projects. In line with the overall project aims, this article presents the ongoing digital transformation activities, courses, and research in the project, which have been carried out by the five partner universities since 2020, and briefly discusses the results. This article presents a summary of the research and educational activities carried out within two parts: complementary research and pilot projects

Reflective Optical Chopper Used in NIST High-Power Laser Measurements

For the past ten years, NIST has used high-reflectivity, optical choppers as beamsplitters and attenuators when calibrating the absolute responsivity and response linearity of detectors used with high-power CW lasers. The chopper-based technique has several advantages over the use of wedge-shaped transparent materials (usually crystals) often used as beam splitters in this type of measurement system. We describe the design, operation and calibration of these choppers. A comparison between choppers and transparent wedge beampslitters is also discussed