A systematic review of machine learning models for predicting outcomes of stroke with structured data

Background and purposeMachine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke.MethodsWe searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. We focused on structured clinical data, excluding image and text analysis. This review was registered with PROSPERO (CRD42019127154).ResultsEighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (5 studies). The most commonly used ML methods were random forests (9 studies), support vector machines (8 studies), decision trees (6 studies), and neural networks (6 studies). The median sample size was 475 (range 70-3184), with a median of 22 predictors (range 4-152) considered. All studies evaluated discrimination with thirteen using area under the ROC curve whilst calibration was assessed in three. Two studies performed external validation. None described the final model sufficiently well to reproduce it.ConclusionsThe use of ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools and none made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before it can meaningfully be considered for practice

Towards a co‐crediting system for carbon and biodiversity

Funder: Royal Botanical Gardens, Kew; doi: http://dx.doi.org/10.13039/501100001296Funder: SPUNFunder: NWO Gravity grant MICROPSocietal Impact StatementHumankind is facing both climate and biodiversity crises. This article proposes the foundations of a scheme that offers tradable credits for combined aboveground and soil carbon and biodiversity. Multidiversity—as estimated based on high‐throughput molecular identification of soil meiofauna, fungi, bacteria, protists, plants and other organisms shedding DNA into soil, complemented by acoustic and video analyses of aboveground macrobiota—offers a cost‐effective method that captures much of the terrestrial biodiversity. Such a voluntary crediting system would increase the quality of carbon projects and contribute funding for delivering the Kunming‐Montreal Global Biodiversity Framework.SummaryCarbon crediting and land offsets for biodiversity protection have been developed to tackle the challenges of increasing greenhouse gas emissions and the loss of global biodiversity. Unfortunately, these two mechanisms are not optimal when considered separately. Focusing solely on carbon capture—the primary goal of most carbon‐focused crediting and offsetting commitments—often results in the establishment of non‐native, fast‐growing monocultures that negatively affect biodiversity and soil‐related ecosystem services. Soil contributes a vast proportion of global biodiversity and contains traces of aboveground organisms. Here, we outline a carbon and biodiversity co‐crediting scheme based on the multi‐kingdom molecular and carbon analyses of soil samples, along with remote sensing estimation of aboveground carbon as well as video and acoustic analyses‐based monitoring of aboveground macroorganisms. Combined, such a co‐crediting scheme could help halt biodiversity loss by incentivising industry and governments to account for biodiversity in carbon sequestration projects more rigorously, explicitly and equitably than they currently do. In most cases, this would help prioritise protection before restoration and help promote more socially and environmentally sustainable land stewardship towards a ‘nature positive’ future.</jats:sec

Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports (‘reference-standard report labels’); a subset of these examinations (n = 250) were assigned ‘reference-standard image labels’ by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-08132-0

Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications