Deployment of Image Analysis Algorithms under Prevalence Shifts

Domain gaps are among the most relevant roadblocks in the clinical translation of machine learning (ML)-based solutions for medical image analysis. While current research focuses on new training paradigms and network architectures, little attention is given to the specific effect of prevalence shifts on an algorithm deployed in practice. Such discrepancies between class frequencies in the data used for a method's development/validation and that in its deployment environment(s) are of great importance, for example in the context of artificial intelligence (AI) democratization, as disease prevalences may vary widely across time and location. Our contribution is twofold. First, we empirically demonstrate the potentially severe consequences of missing prevalence handling by analyzing (i) the extent of miscalibration, (ii) the deviation of the decision threshold from the optimum, and (iii) the ability of validation metrics to reflect neural network performance on the deployment population as a function of the discrepancy between development and deployment prevalence. Second, we propose a workflow for prevalence-aware image classification that uses estimated deployment prevalences to adjust a trained classifier to a new environment, without requiring additional annotated deployment data. Comprehensive experiments based on a diverse set of 30 medical classification tasks showcase the benefit of the proposed workflow in generating better classifier decisions and more reliable performance estimates compared to current practice

Wind Energy and the Turbulent Nature of the Atmospheric Boundary Layer

Wind turbines operate in the atmospheric boundary layer, where they are exposed to the turbulent atmospheric flows. As the response time of wind turbine is typically in the range of seconds, they are affected by the small scale intermittent properties of the turbulent wind. Consequently, basic features which are known for small-scale homogeneous isotropic turbulence, and in particular the well-known intermittency problem, have an important impact on the wind energy conversion process. We report on basic research results concerning the small-scale intermittent properties of atmospheric flows and their impact on the wind energy conversion process. The analysis of wind data shows strongly intermittent statistics of wind fluctuations. To achieve numerical modeling a data-driven superposition model is proposed. For the experimental reproduction and adjustment of intermittent flows a so-called active grid setup is presented. Its ability is shown to generate reproducible properties of atmospheric flows on the smaller scales of the laboratory conditions of a wind tunnel. As an application example the response dynamics of different anemometer types are tested. To achieve a proper understanding of the impact of intermittent turbulent inflow properties on wind turbines we present methods of numerical and stochastic modeling, and compare the results to measurement data. As a summarizing result we find that atmospheric turbulence imposes its intermittent features on the complete wind energy conversion process. Intermittent turbulence features are not only present in atmospheric wind, but are also dominant in the loads on the turbine, i.e. rotor torque and thrust, and in the electrical power output signal. We conclude that profound knowledge of turbulent statistics and the application of suitable numerical as well as experimental methods are necessary to grasp these unique features (...)Comment: Accepted by the Journal of Turbulence on May 17, 201

From a Biomarker to Targeting in a Proof-Of-Concept Trial

Background There is high medical need for safe long-term immunosuppression monotherapy in kidney transplantation. Selective targeting of post-transplant alloantigen-(re)activated effector-T cells by anti-TNF antibodies after global T cell depletion may allow safe drug minimization, however, it is unsolved what might be the best maintenance monotherapy. Methods In this open, prospective observational single-centre trial, 20 primary deceased donor kidney transplant recipients received 2x20 mg Alemtuzumab (d0/d1) followed by 5 mg/kg Infliximab (d2). For 14 days all patients received only tacrolimus, then they were allocated to either receive tacrolimus (TAC, n = 13) or sirolimus (SIR, n = 7) monotherapy, respectively. Protocol biopsies and extensive immune monitoring were performed and patients were followed-up for 60 months. Results TAC-monotherapy resulted in excellent graft survival (5yr 92%, 95%CI: 56.6–98.9) and function, normal histology, and no proteinuria. Immune monitoring revealed low intragraft inflammation (urinary IP-10) and hints for the development of operational tolerance signature in the TAC- but not SIR-group. Remarkably, the TAC-monotherapy was successful in all five presensitized (ELISPOT+) patients. However, recruitment into SIR-arm was stopped (after n = 7) because of high incidence of proteinuria and acute/chronic rejection in biopsies. No opportunistic infections occurred during follow-up. Conclusions In conclusion, our novel fast-track TAC- monotherapy protocol is likely to be safe and preliminary results indicated an excellent 5-year outcome, however, a full–scale study will be needed to confirm our findings. Trial Registration EudraCT Number: 2006-003110-1

Lipopolysaccharide-induced interferon response networks at birth are predictive of severe viral lower respiratory infections in the first year of life

Appropriate innate immune function is essential to limit pathogenesis and severity of severe lower respiratory infections (sLRI) during infancy, a leading cause of hospitalization and risk factor for subsequent asthma in this age group. Employing a systems biology approach to analysis of multi-omic profiles generated from a high-risk cohort (n = 50), we found that the intensity of activation of an LPS-induced interferon gene network at birth was predictive of sLRI risk in infancy (AUC = 0.724). Connectivity patterns within this network were stronger among susceptible individuals, and a systems biology approach identified IRF1 as a putative master regulator of this response. These findings were specific to the LPS-induced interferon response and were not observed following activation of viral nucleic acid sensing pathways. Comparison of responses at birth versus age 5 demonstrated that LPS-induced interferon responses but not responses triggered by viral nucleic acid sensing pathways may be subject to strong developmental regulation. These data suggest that the risk of sLRI in early life is in part already determined at birth, and additionally that the developmental status of LPS-induced interferon responses may be a key determinant of susceptibility. Our findings provide a rationale for the identification of at-risk infants for early intervention aimed at sLRI prevention and identifies targets which may be relevant for drug development

Distinct actin–tropomyosin cofilament populations drive the functional diversification of cytoskeletal myosin motor complexes

The effects of N-terminal acetylation of the high molecular weight tropomyosin isoforms Tpm1.6 and Tpm2.1 and the low molecular weight isoforms Tpm1.12, Tpm3.1 and Tpm4.2 on the actin affinity and the thermal stability of actin-tropomyosin cofilaments are described. Furthermore, we show how the exchange of cytoskeletal tropomyosin isoforms and their N-terminal acetylation affects the kinetic and chemomechanical properties of cytoskeletal actin-tropomyosin-myosin complexes. Our results reveal the extent to which the different actin-tropomyosin-myosin complexes differ in their kinetic and functional properties. The maximum sliding velocity of the actin filament as well as the optimal motor density for continuous unidirectional movement, parameters that were previously considered to be unique and invariant properties of each myosin isoform, are shown to be influenced by the exchange of the tropomyosin isoform and the N-terminal acetylation of tropomyosin

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

Guidelines for Field Surveys of the Quality of Medicines: A Proposal

Paul Newton and colleagues propose guidelines for conducting and reporting field surveys of the quality of medicines

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts