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Over the last two decades, there have been some efforts to improve maternal and child health around the globe. However, the efforts are still lacking longstanding outcomes. Lack of awareness and access to information for decision-making has been an obstacle for women, especially in developing countries. This study aims to find potential strategies to improve maternal and child health using mHealth in Ethiopia to enhance service uptake and future decisions.

DigiONE is a pilot European learning health system in precision oncology that aims to identify optimal cancer treatments by learning from every patient, not just those in trials, through privacy-preserving interrogation of their standardized routine electronic health records.

Federated learning makes it possible to train a machine learning model on decentralized data. Bayesian networks are widely used probabilistic graphical models. While some research has been published on the federated learning of Bayesian networks, publications on Bayesian networks in a vertically partitioned data setting are limited, with important omissions, such as handling missing data. We propose a novel method called VertiBayes to train Bayesian networks (structure and parameters) on vertically partitioned data, which can handle missing values as well as an arbitrary number of parties. For structure learning we adapted the K2 algorithm with a privacy-preserving scalar product protocol. For parameter learning, we use a two-step approach: first, we learn an intermediate model using maximum likelihood, treating missing values as a special value, then we train a model on synthetic data generated by the intermediate model using the EM algorithm. The privacy guarantees of VertiBayes are equivalent to those provided by the privacy preserving scalar product protocol used. We experimentally show VertiBayes produces models comparable to those learnt using traditional algorithms. Finally, we propose two alternative approaches to estimate the performance of the model using vertically partitioned data and we show in experiments that these give accurate estimates.

ABSTRACTDenoising of CT scans has attracted the attention of many researchers in the medical image analysis domain. Encoder–decoder networks are deep learning neural networks that have become common for image denoising in recent years. Shortcuts between the encoder and decoder layers are crucial for some image-to-image translation tasks. However, are all shortcuts necessary for CT denoising? To answer this question, we set up two encoder–decoder networks representing two popular architectures and then progressively removed shortcuts from the networks from shallow to deep (forward removal) and from deep to shallow (backward removal). We used two unrelated datasets with different noise levels to test the denoising performance of these networks using two metrics, namely root mean square error and content loss. The results show that while more than half of the shortcuts are still indispensable for CT scan denoising, removing certain shortcuts leads to performance improvement for denoising. Both shallow and deep shortcuts might be removed, thus retaining sparse connections, especially when the noise level is high. Backward removal seems to have a better performance than forward removal, which means deep shortcuts have priority to be removed. Finally, we propose a hypothesis to explain this phenomenon and validate it in the experiments.

•PROMs are more predictive than clinical information for endpoints like erectile dysfunction.•Patient perspective should be incorporated in outcome research, such as erectile dysfunction, since the patient understands the disease symptoms better.•Experts' involvement in clinical research is critical as it aligns the modeling steps with the clinical process for increased clinical plausibility.

Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.

Background: Hemodynamic assistance with a miniature intracardiac pump may fill the treatment gap between use of an intraaortic balloon pump (IABP) and the current, more invasive ventricular assist devices. The [...]

Objective: To compare the efficacy of a new intraaortic propeller pump (PP) to provide hemodynamic support to the intraaortic balloon pump (IABP) in an acute mitral regurgitation (MR) animal model. [...]

Cancer is a fatal disease and one of the leading causes of death worldwide. The cure rate in cancer treatment remains low; hence, cancer treatment is gradually shifting toward personalized treatment. Artificial intelligence (AI) and radiomics have been recognized as one of the potential areas of research in personalized medicine in oncology. Several researchers have identified the capabilities of AI and radiomics to characterize phenotype and there by predict the outcome of treatment in oncology. Although AI and radiomics have shown promising initial results in diagnosis and treatment in oncology, these technologies are also facing challenges of standardization and scalability. In the last few years, researchers have been trying to develop a research infrastructure for federated machine learning that increases the usability of Big Data for clinical research. These research infrastructures are based on the findable, accessible, interoperable, and reusable (i.e., FAIR) data principles. The India-Dutch “big imaging data approach for oncology in a Netherlands India collaboration” (BIONIC) is a jointly funded initiative by the Dutch Research Council (NWO) and the Indian Ministry of Electronics and Information Technology (MeitY), aiming to introduce radiomic-based research into clinical environments using federated machine learning on geographically dispersed collections of FAIR data. This article described a prototype end-to-end research infrastructure implemented through the BIONIC partnership into a leading cancer care public hospital in India.

Artificial intelligence (AI) applications have recently been proposed to detect errors in radiotherapy plans. External validation of such systems is essential to assess their performance and safety before applying them to clinical practice. We collected data from 5238 patients treated at Maastro Clinic and introduced a range of common radiotherapy plan errors for the model to detect. We estimated the model’s discrimination by calculating the area under the receiver-operating characteristic curve (AUC). We also assessed its clinical usefulness as an alert system that could reduce the need for manual checks by calculating the percentage of values flagged as errors and the positive predictive value (PPV) for a range of high sensitivities (95%–99%) and error prevalence. The AUC when considering all variables was 67.8% (95% CI, 65.6%–69.9%). The AUC varied widely for different types of errors (from 90.4% for table angle errors to 54.5% for planning tumor volume-PTV dose errors). The percentage of flagged values ranged from 84% to 90% for sensitivities between 95% and 99% and the PPV was only slightly higher than the prevalence of the errors. The model’s performance in the external validation was significantly worse than that in its original setting (AUC of 68% versus 89%). Its usefulness as an alert system to reduce the need for manual checks is questionable due to the low PPV and high percentage of values flagged as potential errors to achieve a high sensitivity. We analyzed the apparent limitations of the model and we proposed actions to overcome them.

Introduction: Poor child feeding practice is a public health problem in Africa. Mobile health (mHealth) is a supportive intervention to improve this problem; however, the evidence available in the current literature is inconsistent and inconclusive in Africa. Some studies state that exclusive breastfeeding is not different between controls and mHealth interventions in the first month. Other studies state that health providers need additional training for the success of mHealth interventions. Objective: This systematic review and meta-analysis aims to provide the summarized effect of mHealth on child-feeding practices in Africa to improve future planning and decisions. Method: We conducted a systematic review and meta-analysis based on the published and unpublished evidence gathered from PubMed, Web of Science, Cochrane Library, and Embase databases between January 1, 2000, and March 1, 2022. Studies included were randomized control trials and experimental studies that compared mHealth to standards of care among postpartum women. Preferred Reporting Items for Systematic Review and Meta-analysis guidelines followed for the reporting. Results: After screening 1188 studies, we identified six studies that fulfilled the study criteria. These studies had 2913 participants with the number of total intervention groups 1627 [1627/2913 = 56%]. Five studies were completed within 24 weeks while one required 12 weeks. We included two RCTs, two cluster RCTs, and two quasi-experimental studies all used mHealth as the major intervention and usual care as controls. We found significant improvement in child-feeding practices among intervention groups. Conclusion: This systematic review and meta-analysis showed that the application of mHealth improved child-feeding practices in Africa. Although the finding is compelling, the authors recommend high-quality studies and mHealth interventions that consider sample size, design, regional differences, and environmental constraints to enhance policy decisions. The place of residence, access, low socioeconomic development, poor socio-demographic characteristics, low women empowerment, and low women’s education might cause high heterogeneity in the included regions and need consideration during interventions.

Radiomics is becoming increasingly more important in medical imagingThe explosion of medical imaging data creates an environment ideal for machine-learning and data-based scienceRadiomics-based decision-support systems for precision diagnosis and treatment can be a powerful tool in modern medicineLarge-scale data sharing is necessary for the validation and full potential that radiomics representsStandardized data collection, evaluation criteria, and reporting guidelines are required for radiomics to mature as a discipline

SUMMARYThe interaction between implementation of new technologies and different outcomes can allow a broad range of researches to be expanded. The purpose of this paper is to introduce the VAlidation of high TEchnology based on large database analysis by learning machine (VATE) project that aims to combine new technologies with outcomes related to rectal cancer in terms of tumor control and normal tissue sparing. Using automated computer bots and the knowledge for screening data it is possible to identify the factors that can mostly influence those outcomes. Population-based observational studies resulting from the linkage of different datasets will be conducted in order to develop predictive models that allow physicians to share decision with patients into a wider concept of tailored treatment.

The emergence of individualized medicine has spurred the need for the development of clinical decision-support systems (CDSSs) based on prediction models of treatment outcome. In radiation oncology, CDSSs combine clinical, imaging and molecular factors to achieve the highest accuracy to predict tumour response. Here, the authors provide an overview of these factors—including survival, recurrence patterns and toxicity—and discuss the methodology behind the multistage development of CDSSs.