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

Revolutionizing Clinical Microbiology Laboratory Organization in Hospitals with In Situ Point-of-Care

BACKGROUND: Clinical microbiology may direct decisions regarding hospitalization, isolation and anti-infective therapy, but it is not effective at the time of early care. Point-of-care (POC) tests have been developed for this purpose. METHODS AND FINDINGS: One pilot POC-lab was located close to the core laboratory and emergency ward to test the proof of concept. A second POC-lab was located inside the emergency ward of a distant hospital without a microbiology laboratory. Twenty-three molecular and immuno-detection tests, which were technically undemanding, were progressively implemented, with results obtained in less than four hours. From 2008 to 2010, 51,179 tests yielded 6,244 diagnoses. The second POC-lab detected contagious pathogens in 982 patients who benefited from targeted isolation measures, including those undertaken during the influenza outbreak. POC tests prevented unnecessary treatment of patients with non-streptococcal tonsillitis (n = 1,844) and pregnant women negative for Streptococcus agalactiae carriage (n = 763). The cerebrospinal fluid culture remained sterile in 50% of the 49 patients with bacterial meningitis, therefore antibiotic treatment was guided by the molecular tests performed in the POC-labs. With regard to enterovirus meningitis, the mean length-of-stay of infected patients over 15 years old significantly decreased from 2008 to 2010 compared with 2005 when the POC was not in place (1.43±1.09 versus 2.91±2.31 days; p = 0.0009). Altogether, patients who received POC tests were immediately discharged nearly thrice as often as patients who underwent a conventional diagnostic procedure. CONCLUSIONS: The on-site POC-lab met physicians' needs and influenced the management of 8% of the patients that presented to emergency wards. This strategy might represent a major evolution of decision-making regarding the management of infectious diseases and patient care

Construction, assembly and tests of the ATLAS electromagnetic barrel calorimeter

The construction and assembly of the two half barrels of the ATLAS central electromagnetic calorimeter and their insertion into the barrel cryostat are described. The results of the qualification tests of the calorimeter before installation in the LHC ATLAS pit are given

Performance of the ATLAS Electromagnetic Calorimeter End-cap Module 0

The construction and beam test results of the ATLAS electromagnetic end-cap calorimeter pre-production module 0 are presented. The stochastic term of the energy resolution is between 10% GeV^1/2 and 12.5% GeV^1/2 over the full pseudorapidity range. Position and angular resolutions are found to be in agreement with simulation. A global constant term of 0.6% is obtained in the pseudorapidity range 2.5 < eta < 3.2 (inner wheel)

Performance of the ATLAS electromagnetic calorimeter end-cap module 0

The construction and beam test results of the ATLAS electromagnetic end-cap calorimeter pre-production module 0 are presented. The stochastic term of the energy resolution is between 10% GeV^1/2 and 12.5% GeV^1/2 over the full pseudorapidity range. Position and angular resolutions are found to be in agreement with simulation. A global constant term of 0.6% is obtained in the pseudorapidity range 2.5 eta 3.2 (inner wheel)

Static and dynamic analysis of bending–torsion coupling of a CFRP sandwich beam

International audienceThe bending-torsion coupling for a sandwich beam is analysed from the static and vibrational point of view. Off-axis orientations in the skin and transverse graded core are proposed to perform the coupling. Solutions are first compared numerically from a static point of view using a complete beam theory. The natural frequencies and mode shapes are then studied experimentally and numerically. An experimental assembly test rig is presented, where a sandwich beam is embedded on the shaker and measurements are taken with a scanning vibrometer. These results are then compared to those calculated by a finite element model in order to verify the accuracy of the model and its ability to consider all the physical phenomena involved

Recyclage du PET par méthodes chimiques

Jeu de donnée

Imagerie Métabolique Rapide de Molécules Hyperpolarisées : à la Recherche du Temps Perdu

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