Clinical Data Analytics & Radiomics
Harnessing artificial intelligence in radiology to augment population health
This review article serves to highlight radiological services as a major cost driver for the healthcare sector, and the potential improvements in productivity and cost savings that can be generated by incorporating artificial intelligence (AI) into the radiology workflow, referencing Singapore healthcare as an example.
ReadMultigenerational adversity impacts on human gut microbiome composition and socioemotional functioning in early childhood
This study draws on a large longitudinal cohort to demonstrate that adversity experienced prenatally or during early childhood, as well as adversity experienced by the mother during her childhood, impacts the gut microbiome of second-generation children at 2 y old. Notably, some of the microbiome profiles linked to these types of adversity, especially at higher taxonomic levels, were similar to those associated with the child’s current and future socioemotional functioning. Additionally, microbes uniquely associated with adversity exposures or socioemotional functioning have similar immune-related functions within the gut, highlighting the need for further research into how generational adversity affects the gut microbiome’s functional potential.
ReadAn end-end deep learning framework for lesion segmentation on multi-contrast MR images—an exploratory study in a rat model of traumatic brain injury
Keywords: Controlled cortical impact; Deep learning; Global attention; Segmentation; Self-attention; Traumatic brain injury; U-Net.
ReadNeuroanatomical subtypes of schizophrenia and relationship with illness duration and deficit status
Schizophrenia (SCZ) is a chronic and serious mental illness affecting >27 million worldwide (GBD 2017 Disease and Injury Incidence and Prevalence Collaborators, 2018). Heterogeneity in neurobiology (Voineskos et al., 2020), polygenic scores (Alnæs et al., 2019), symptom presentations (Van Rheenen et al., 2017), treatment responses (Malhotra, 2015), and outcomes (Huber, 1997) and their inter-relationships remain incompletely understood and can potentially confound evaluation and affect management of the condition (Lakhan and Vieira, 2009). Grouping patients by clinical subtypes allows for a deeper examination of homogenous groups, and this is often done using measures which assess symptomatology (e.g positive/negative symptom domains), cognitive status or illness course (e.g remission status) (Tan et al., 2020; Weinberg et al., 2016; Wong et al., 2020). Previous studies have attempted to link these clinical subtypes in SCZ based on deficit syndrome (Tan et al., 2020), cognitive functioning (Ho et al., 2020; Seaton et al., 2001; Weinberg et al., 2016) and remission status (Wong et al., 2020) with underlying neural substrates.
ReadCAFT: a deep learning‑based comprehensive abdominal fat analysis tool for large cohort studies
Background There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and stroke to also impact upon the muscle to give rise to sarcopenic obesity. Phenotypic knowledge of obesity is crucial for profiling and management of obesity, as different fat—subcutaneous adipose tissue depots (SAT) and visceral adipose tissue depots (VAT) have various degrees of influence on metabolic syndrome and morbidities. Manual segmentation is time consuming and laborious. Study focuses on the development of a deep learning-based, complete data processing pipeline for MRI-based fat analysis, for large cohort studies which include (1) data augmentation and preprocessing (2) model zoo (3) visualization dashboard, and (4) correction tool, for automated quantification of fat compartments SAT and VAT.
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