To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT.
We retrospectively identified all ...whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455).
All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level per CT scan: rib fracture(s): yes/no. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement.
We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.
A dysregulated immune response with hyperinflammation is observed in patients with severe coronavirus disease 2019 (COVID-19). The aim of the present study was to assess the safety and potential ...benefits of human recombinant C1 esterase inhibitor (conestat alfa), a complement, contact activation and kallikrein-kinin system regulator, in severe COVID-19. Patients with evidence of progressive disease after 24 h including an oxygen saturation <93% at rest in ambient air were included at the University Hospital Basel, Switzerland in April 2020. Conestat alfa was administered by intravenous injections of 8400 IU followed by 3 additional doses of 4200 IU in 12-h intervals. Five patients (age range, 53-85 years; one woman) with severe COVID-19 pneumonia (11-39% lung involvement on computed tomography scan of the chest) were treated a median of 1 day (range 1-7 days) after admission. Treatment was well-tolerated. Immediate defervescence occurred, and inflammatory markers and oxygen supplementation decreased or stabilized in 4 patients (e.g., median C-reactive protein 203 (range 31-235) mg/L before vs. 32 (12-72) mg/L on day 5). Only one patient required mechanical ventilation. All patients recovered. C1INH concentrations were elevated before conestat alfa treatment. Levels of complement activation products declined after treatment. Viral loads in nasopharyngeal swabs declined in 4 patients. In this uncontrolled case series, targeting multiple inflammatory cascades by conestat alfa was safe and associated with clinical improvements in the majority of severe COVID-19 patients. Controlled clinical trials are needed to assess its safety and efficacy in preventing disease progression.
Not pulmonary factors, but physical deconditioning is the main limiting factor of exercise capacity in patients after severe COVID-19 pneumonitis. This underscores the importance of an early ...rehabilitative intervention in these patients.
https://bit.ly/2XVvr6C
Historically, giant cell arteritis (GCA) was considered to be synonymous with temporal arteritis. However, the disease spectrum of GCA extends much further, and includes vasculitis of the aorta and ...its branches with or without involvement of the temporal arteries. Imaging is crucial for the diagnosis and follow-up of GCA patients. Large vessel GCA (LV-GCA) often presents as an inflammatory syndrome and is only detected by imaging modalities such as: colour duplex sonography (CDS), computed tomography (CT) / CT angiography (CTA), magnetic resonance imaging (MRI) or 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) / CT. Deciding which imaging modality to use in different clinical situations remains a matter of debate. CDS and MRI enable assessment of the temporal arteries with a presumably higher sensitivity than histology. In the context of a typical presentation, CDS can replace a biopsy. In about a third of patients, the temporal arteries are not involved, thus PET/CT, MRI, CT, or CDS of larger arteries is needed to diagnose GCA. The sensitivity of all modalities is affected by glucocorticoid therapy. Therefore, without delaying therapy, imaging should be performed within a few days of treatment initiation. The use of PET/CT for the work-up of inflammatory syndromes in the elderly reveals vasculitis in approximately 20% of examined patients and uncover relevant diagnoses in the majority of remaining patients. The aorta should be routinely assessed in all GCA patients at diagnosis and during follow-up. MRA or CTA are best suited to characterise structural damage of larger arteries. The role of imaging in monitoring GCA disease activity still needs to be further defined.
Atrial fibrillation (AF) has been linked to left atrial (LA) enlargement. Whereas most studies focused on 2D-based estimation of static LA volume (LAV), we used a fully-automatic convolutional neural ...network (CNN) for time-resolved (CINE) volumetry of the whole LA on cardiac MRI (cMRI). Aim was to investigate associations between functional parameters from fully-automated, 3D-based analysis of the LA and current classification schemes in AF. We retrospectively analyzed consecutive AF patients who underwent cMRI on 1.5T systems including a stack of oblique-axial CINE series covering the whole LA. The LA was automatically segmented by a validated CNN. In the resulting volume-time curves, maximum, minimum and LAV before atrial contraction were automatically identified. Active, passive and total LA emptying fractions (LAEF) were calculated and compared to clinical classifications (AF Burden score (AFBS), increased stroke risk (CHA.sub.2 DS.sub.2 VAScgreater than or equal to2), AF type (paroxysmal/persistent), EHRA score, and AF risk factors). Moreover, multivariable linear regression models (mLRM) were used to identify associations with AF risk factors. Overall, 102 patients (age 61±9 years, 17% female) were analyzed. Active LAEF (LAEF_active) decreased significantly with an increase of AFBS (minimal: 44.0%, mild: 36.2%, moderate: 31.7%, severe: 20.8%, p<0.003) which was primarily caused by an increase of minimum LAV. Likewise, LAEF_active was lower in patients with increased stroke risk (30.7% vs. 38.9%, p = 0.002). AF type and EHRA score did not show significant differences between groups. In mLRM, a decrease of LAEF_active was associated with higher age (per year: -0.3%, p = 0.02), higher AFBS (per category: -4.2%, p<0.03) and heart failure (-12.1%, p<0.04). Fully-automatic morphometry of the whole LA derived from cMRI showed significant relationships between LAEF_active with increased stroke risk and severity of AFBS. Furthermore, higher age, higher AFBS and presence of heart failure were independent predictors of reduced LAEF_active, indicating its potential usefulness as an imaging biomarker.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left ...atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process.
Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m
with upper and lower limits of agreement of - 7.5 and 5.8 mL/m
, respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%-from 105 to 34 s, in our in-house clinical setting.
Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results.
To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to ...predict patient management.
All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit ICU). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans.
While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve AUC = 0.88; 95% confidence interval CI = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88).
Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.
Langerhans cell histiocytosis (LCH) commonly co-occurs with additional myeloid malignancies. The introduction of targeted therapies, blocking "driver" mutations (e.g.,
), enabled long-term remission ...in patients with LCH. The effect of
inhibition on the course and the prognosis of co-existing clonal hematopoiesis is poorly understood. We report on a 61-year-old patient with systemic
positive LCH and concomitant
wild-type (wt) clonal cytopenia of unknown significance (CCUS) with unfavorable somatic mutations including loss of function (LOF) of
. While manifestations of LCH improved after blocking
by dabrafenib treatment, the
wt CCUS progressed to acute myeloid leukemia (AML). The patient eventually underwent successful allogeneic hematopoietic stem cell transplantation (HSCT). We performed an in-depth analyzes of the clonal relationship of CCUS and the tissue affected by LCH by using next-generation sequencing (NGS). The findings suggest activation of the mitogen-activated protein (MAP) kinase pathway in the CCUS clone due to the presence of the
deregulating
mutations and wt
, which is reportedly associated with paradoxical activation of
and hence
. Patients with LCH should be carefully screened for potential additional clonal hematological diseases. NGS can help predict outcome of the latter in case of
inhibition. Blocking the MAP kinase pathway further downstream (e.g., by using MEK inhibitors) or allogeneic HSCT may be options for patients at risk.
Abstract Objective To investigate the potential of MRI for lung nodule detection in a high-risk population in comparison to low-dose CT. Methods 49 participants (31 men, 18 women, 51–71 years) of the ...German Lung Cancer Screening and Intervention Trial (LUSI) with a cancer-suspicious lung lesion in CT were examined with non-contrast-enhanced MRI of the lung at 1.5 T. Data were pseudonymized and presented at random order together with 30 datasets (23 in men, 7 in women, 18–64 years) from healthy volunteers. Two radiologists read the data for the presence of nodules. Sensitivity and specificity were calculated. Gold standard was either histology or long-term follow-up. Contrast-to-Noise-Ratio (CNR) was measured for all detected lesions in all MRI sequences. Results Average maximum diameter of the lesions was 15 mm. Overall sensitivity and specificity of MRI were 48% (26/54) and 88% (29/33) compared to low-dose CT. Sensitivity of MRI was significantly higher for malignant nodules (78% (12.5/16)) than for benign ones (36% (13.5/38); P = 0.007). There was no statistically significant difference in sensitivity between nodules (benign and malignant) larger or smaller than 10 mm ( P = 0.7). Inter observer agreement was 84% ( κ = 0.65). Lesion-to-background CNR of T2-weighted single-shot turbo-spin-echo was significantly higher for malignant nodules (89 ± 27) than for benign ones (56 ± 23; P = 0.002). Conclusion The sensitivity of MRI for detection of malignant pulmonary nodules in a high-risk population is 78%. Due to its inherent soft tissue contrast, MRI is more sensitive to malignant nodules than to benign ones. MRI may therefore represent a useful test for early detection of lung cancer.
Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We ...retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level per CT scan: rib fracture(s): yes/no. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.
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