191 research outputs found
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction
We present a multi-stage 3D computer-aided detection and diagnosis (CAD)
model for automated localization of clinically significant prostate cancer
(csPCa) in bi-parametric MR imaging (bpMRI). Deep attention mechanisms drive
its detection network, targeting salient structures and highly discriminative
feature dimensions across multiple resolutions. Its goal is to accurately
identify csPCa lesions from indolent cancer and the wide range of benign
pathology that can afflict the prostate gland. Simultaneously, a decoupled
residual classifier is used to achieve consistent false positive reduction,
without sacrificing high sensitivity or computational efficiency. In order to
guide model generalization with domain-specific clinical knowledge, a
probabilistic anatomical prior is used to encode the spatial prevalence and
zonal distinction of csPCa. Using a large dataset of 1950 prostate bpMRI paired
with radiologically-estimated annotations, we hypothesize that such CNN-based
models can be trained to detect biopsy-confirmed malignancies in an independent
cohort.
For 486 institutional testing scans, the 3D CAD system achieves
83.695.22% and 93.192.96% detection sensitivity at 0.50 and 1.46
false positive(s) per patient, respectively, with 0.8820.030 AUROC in
patient-based diagnosis significantly outperforming four state-of-the-art
baseline architectures (U-SEResNet, UNet++, nnU-Net, Attention U-Net) from
recent literature. For 296 external biopsy-confirmed testing scans, the
ensembled CAD system shares moderate agreement with a consensus of expert
radiologists (76.69%; 0.510.04) and independent pathologists
(81.08%; 0.560.06); demonstrating strong generalization to
histologically-confirmed csPCa diagnosis.Comment: Accepted to MedIA: Medical Image Analysis. This manuscript
incorporates and expands upon our 2020 Medical Imaging Meets NeurIPS Workshop
paper (arXiv:2011.00263
Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms
Currently, diagnosis of skin diseases is based primarily on visual pattern
recognition skills and expertise of the physician observing the lesion. Even
though dermatologists are trained to recognize patterns of morphology, it is
still a subjective visual assessment. Tools for automated pattern recognition
can provide objective information to support clinical decision-making.
Noninvasive skin imaging techniques provide complementary information to the
clinician. In recent years, optical coherence tomography has become a powerful
skin imaging technique. According to specific functional needs, skin
architecture varies across different parts of the body, as do the textural
characteristics in OCT images. There is, therefore, a critical need to
systematically analyze OCT images from different body sites, to identify their
significant qualitative and quantitative differences. Sixty-three optical and
textural features extracted from OCT images of healthy and diseased skin are
analyzed and in conjunction with decision-theoretic approaches used to create
computational models of the diseases. We demonstrate that these models provide
objective information to the clinician to assist in the diagnosis of
abnormalities of cutaneous microstructure, and hence, aid in the determination
of treatment. Specifically, we demonstrate the performance of this methodology
on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)
from healthy tissue
Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI
Deep learning-based diagnostic performance increases with more annotated
data, but large-scale manual annotations are expensive and labour-intensive.
Experts evaluate diagnostic images during clinical routine, and write their
findings in reports. Leveraging unlabelled exams paired with clinical reports
could overcome the manual labelling bottleneck. We hypothesise that detection
models can be trained semi-supervised with automatic annotations generated
using model predictions, guided by sparse information from clinical reports. To
demonstrate efficacy, we train clinically significant prostate cancer (csPCa)
segmentation models, where automatic annotations are guided by the number of
clinically significant findings in the radiology reports. We included 7,756
prostate MRI examinations, of which 3,050 were manually annotated. We evaluated
prostate cancer detection performance on 300 exams from an external centre with
histopathology-confirmed ground truth. Semi-supervised training improved
patient-based diagnostic area under the receiver operating characteristic curve
from to () and improved
lesion-based sensitivity at one false positive per case from
to (). Semi-supervised training was 14 more
annotation-efficient for case-based performance and 6 more
annotation-efficient for lesion-based performance. This improved performance
demonstrates the feasibility of our training procedure. Source code is publicly
available at github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCa
detection algorithm is available at
grand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/
Past, present, and future of global health financing: a review of development assistance, government, out-of-pocket, and other private spending on health for 195 countries, 1995–2050
© 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Background: Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. Methods: We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories—government, out-of-pocket, and prepaid private health spending—and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. Findings: Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89–4·12) annually, although it grew slower in per capita terms (2·72% [2·61–2·84]) and increased by less than 8·0 trillion (7·8–8·1) in 2016 (comprising 8·6% [8·4–8·7] of the global economy and 5252 (5184–5319) in high-income countries, 81 (74–89) in lower-middle-income countries, and 9·5 billion, 24·3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH (15·0 trillion (14·0–16·0) by 2050 (reaching 9·4% [7·6–11·3] of the global economy and $21·3 trillion [19·8–23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1·84% (1·68–2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0·6% (0·6–0·7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130·2 (122·9–136·9) in 2016 and is projected to remain at similar levels in 2050 (125·9 [113·7–138·1]). The decomposition analysis identified governments’ increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. Interpretation: Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets. Funding: Bill & Melinda Gates Foundation
Past, present, and future of global health financing : a review of development assistance, government, out-of-pocket, and other private spending on health for 195 countries, 1995-2050
Background Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. Methods We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories-government, out-of-pocket, and prepaid private health spending-and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. Findings Between 1995 and 2016, health spending grew at a rate of 4.00% (95% uncertainty interval 3.89-4.12) annually, although it grew slower in per capita terms (2.72% [2.61-2.84]) and increased by less than 8.0 trillion (7.8-8.1) in 2016 (comprising 8.6% [8.4-8.7] of the global economy and 5252 (5184-5319) in high-income countries, 81 (74-89) in lower-middle-income countries, and 9.5 billion, 24.3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6.27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH ( 15.0 trillion (14.0-16.0) by 2050 (reaching 9.4% [7.6-11.3] of the global economy and $ 21.3 trillion [19.8-23.1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1.84% (1.68-2.02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0.6% (0.6-0.7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15.7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130.2 (122.9-136.9) in 2016 and is projected to remain at similar levels in 2050 (125.9 [113.7-138.1]). The decomposition analysis identified governments' increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. Interpretation Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets.Peer reviewe
Past, present, and future of global health financing: a review of development assistance, government, out-of-pocket, and other private spending on health for 195 countries, 1995–2050
Background: Comprehensive and comparable estimates of health spending in each country are a key input for health
policy and planning, and are necessary to support the achievement of national and international health goals. Previous
studies have tracked past and projected future health spending until 2040 and shown that, with economic development,
countries tend to spend more on health per capita, with a decreasing share of spending from development assistance
and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending,
with an emphasis on equity in spending across countries.
Methods: We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three
categories—government, out-of-pocket, and prepaid private health spending—and estimated development assistance
for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear
mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050
and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and
revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data
were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private,
and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power
parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition
methods to assess a set of factors associated with changes in government health spending between 1995 and 2016
and to examine evidence to support the theory of the health financing transition. We projected two alternative future
scenarios based on higher government health spending to assess the potential ability of governments to generate
more resources for health.
Findings: Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89–4·12)
annually, although it grew slower in per capita terms (2·72% [2·61–2·84]) and increased by less than 8·0 trillion (7·8–8·1) in 2016 (comprising 8·6% [8·4–8·7] of the global economy and 5252 (5184–5319) in high-income
countries, 81 (74–89) in lower-middle-income countries, and
9·5 billion, 24·3% of total DAH), although spending on other infectious diseases
(excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH
were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation).
For the first time, we included estimates of China’s contribution to DAH (15·0 trillion (14·0–16·0) by 2050 (reaching 9·4% [7·6–11·3] of the global
economy and $21·3 trillion [19·8–23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of
1·84% (1·68–2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate
that 0·6% (0·6–0·7) of health spending will occur in currently low-income countries, despite these countries
comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in
high-income and low-income countries was 130·2 (122·9–136·9) in 2016 and is projected to remain at similar levels
in 2050 (125·9 [113·7–138·1]). The decomposition analysis identified governments’ increased prioritisation of the
health sector and economic development as the strongest factors associated with increases in government health
spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the
health sector and increased government spending, health spending per capita could more than double, with greater
impacts in countries that currently have the lowest levels of government health spending
Interpretation: Financing for global health has increased steadily over the past two decades and is projected to continue
increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health
spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income
countries. Many low-income countries are expected to remain dependent on development assistance, although with
greater government spending, larger investments in health are feasible. In the absence of sustained new investments
in health, increasing efficiency in health spending is essential to meet global health targets.
Funding: Bill & Melinda Gates Foundatio
The global distribution of lymphatic filariasis, 2000–18: a geospatial analysis
Background
Lymphatic filariasis is a neglected tropical disease that can cause permanent disability through disruption of the lymphatic system. This disease is caused by parasitic filarial worms that are transmitted by mosquitos. Mass drug administration (MDA) of antihelmintics is recommended by WHO to eliminate lymphatic filariasis as a public health problem. This study aims to produce the first geospatial estimates of the global prevalence of lymphatic filariasis infection over time, to quantify progress towards elimination, and to identify geographical variation in distribution of infection.
Methods
A global dataset of georeferenced surveyed locations was used to model annual 2000–18 lymphatic filariasis prevalence for 73 current or previously endemic countries. We applied Bayesian model-based geostatistics and time series methods to generate spatially continuous estimates of global all-age 2000–18 prevalence of lymphatic filariasis infection mapped at a resolution of 5 km2 and aggregated to estimate total number of individuals infected.
Findings
We used 14 927 datapoints to fit the geospatial models. An estimated 199 million total individuals (95% uncertainty interval 174–234 million) worldwide were infected with lymphatic filariasis in 2000, with totals for WHO regions ranging from 3·1 million (1·6–5·7 million) in the region of the Americas to 107 million (91–134 million) in the South-East Asia region. By 2018, an estimated 51 million individuals (43–63 million) were infected. Broad declines in prevalence are observed globally, but focal areas in Africa and southeast Asia remain less likely to have attained infection prevalence thresholds proposed to achieve local elimination.
Interpretation
Although the prevalence of lymphatic filariasis infection has declined since 2000, MDA is still necessary across large populations in Africa and Asia. Our mapped estimates can be used to identify areas where the probability of meeting infection thresholds is low, and when coupled with large uncertainty in the predictions, indicate additional data collection or intervention might be warranted before MDA programmes cease
Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: progress towards Sustainable Development Goal 3
Background: Sustainable Development Goal (SDG) 3 aims to “ensure healthy lives and promote well-being for all at all
ages”. While a substantial effort has been made to quantify progress towards SDG3, less research has focused on
tracking spending towards this goal. We used spending estimates to measure progress in financing the priority areas
of SDG3, examine the association between outcomes and financing, and identify where resource gains are most
needed to achieve the SDG3 indicators for which data are available.
Methods: We estimated domestic health spending, disaggregated by source (government, out-of-pocket, and prepaid
private) from 1995 to 2017 for 195 countries and territories. For disease-specific health spending, we estimated
spending for HIV/AIDS and tuberculosis for 135 low-income and middle-income countries, and malaria in
106 malaria-endemic countries, from 2000 to 2017. We also estimated development assistance for health (DAH) from
1990 to 2019, by source, disbursing development agency, recipient, and health focus area, including DAH for
pandemic preparedness. Finally, we estimated future health spending for 195 countries and territories from 2018 until
2030. We report all spending estimates in inflation-adjusted 2019 US7·9 trillion (95% uncertainty interval 7·8–8·0) in 2017 and is expected to increase to 20·2 billion
(17·0–25·0) and on tuberculosis it was 5·1 billion (4·9–5·4). Development assistance for health was 374 million of DAH was provided
for pandemic preparedness, less than 1% of DAH. Although spending has increased across HIV/AIDS, tuberculosis,
and malaria since 2015, spending has not increased in all countries, and outcomes in terms of prevalence, incidence,
and per-capita spending have been mixed. The proportion of health spending from pooled sources is expected to
increase from 81·6% (81·6–81·7) in 2015 to 83·1% (82·8–83·3) in 2030.
Interpretation: Health spending on SDG3 priority areas has increased, but not in all countries, and progress towards
meeting the SDG3 targets has been mixed and has varied by country and by target. The evidence on the scale-up of
spending and improvements in health outcomes suggest a nuanced relationship, such that increases in spending do
not always results in improvements in outcomes. Although countries will probably need more resources to achieve
SDG3, other constraints in the broader health system such as inefficient allocation of resources across interventions
and populations, weak governance systems, human resource shortages, and drug shortages, will also need to be
addressed.
Funding: The Bill & Melinda Gates Foundatio
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