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Pediatric Research Oct 2022Lung ultrasound (LUS) for critical patients requires trained operators to perform them, though little information exists on the level of training required for...
BACKGROUND
Lung ultrasound (LUS) for critical patients requires trained operators to perform them, though little information exists on the level of training required for independent practice. The aims were to implement a training plan for diagnosing pneumonia using LUS and to analyze the inter-observer agreement between senior radiologists (SRs) and pediatric intensive care physicians (PICPs).
METHODS
Prospective longitudinal and interventional study conducted in the Pediatric Intensive Care Unit of a tertiary hospital. Following a theoretical and practical training plan regarding diagnosing pneumonia using LUS, the concordance between SRs and the PICPs on their LUS reports was analyzed.
RESULTS
Nine PICPs were trained and tested on both theoretical and practical LUS knowledge. The mean exam mark was 13.5/15. To evaluate inter-observer agreement, a total of 483 LUS were performed. For interstitial syndrome, the global Kappa coefficient (K) was 0.51 (95% CI 0.43-0.58). Regarding the presence of consolidation, K was 0.67 (95% CI 0.53-0.78), and for the consolidation pattern, K was 0.82 (95% CI 0.79-0.85), showing almost perfect agreement.
CONCLUSIONS
Our training plan allowed PICPs to independently perform LUS and might improve pneumonia diagnosis. We found a high inter-observer agreement between PICPs and SRs in detecting the presence and type of consolidation on LUS.
IMPACT
Lung ultrasound (LUS) has been proposed as an alternative to diagnose pneumonia in children. However, the adoption of LUS in clinical practice has been slow, and it is not yet included in general clinical guidelines. The results of this study show that the implementation of a LUS training program may improve pneumonia diagnosis in critically ill patients. The training program's design, implementation, and evaluation are described. The high inter-observer agreement between LUS reports from the physicians trained and expert radiologists encourage the use of LUS not only for pneumonia diagnosis, but also for discerning bacterial and viral patterns.
Topics: Child; Humans; Prospective Studies; Pneumonia; Lung; Ultrasonography; Lung Diseases
PubMed: 34969992
DOI: 10.1038/s41390-021-01928-2 -
Computer Methods and Programs in... Oct 2022Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has...
OBJECTIVE
Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival.
METHODS
The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques.
RESULTS
The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness.
CONCLUSION
With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.
Topics: COVID-19; COVID-19 Testing; Humans; Image Processing, Computer-Assisted; Lung; Neural Networks, Computer
PubMed: 35964421
DOI: 10.1016/j.cmpb.2022.107053 -
Journal of Translational Medicine Jan 2021Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as...
BACKGROUND
Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input.
METHODS
All histopathologically confirmed endometrial lesion images were obtained from the Shengjing Hospital of China Medical University, including endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyps, and submucous myomas. The study included 1851 images from 454 patients. After the images were preprocessed (histogram equalization, addition of noise, rotations, and flips), a training set of 6478 images was input into a tuned VGGNet-16 model; 250 images were used as the test set to evaluate the model's performance. Thereafter, we compared the model's results with the diagnosis of gynecologists.
RESULTS
The overall accuracy of the VGGNet-16 model in classifying endometrial lesions is 80.8%. Its sensitivity to endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyp, and submucous myoma is 84.0%, 68.0%, 78.0%, 94.0%, and 80.0%, respectively; for these diagnoses, the model's specificity is 92.5%, 95.5%, 96.5%, 95.0%, and 96.5%, respectively. When classifying lesions as benign or as premalignant/malignant, the VGGNet-16 model's accuracy, sensitivity, and specificity are 90.8%, 83.0%, and 96.0%, respectively. The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved.
CONCLUSIONS
The VGGNet-16 model performs well in classifying endometrial lesions from hysteroscopic images and can provide objective diagnostic evidence for hysteroscopists.
Topics: China; Deep Learning; Endometrial Hyperplasia; Endometrial Neoplasms; Female; Humans; Hysteroscopy; Pregnancy; Sensitivity and Specificity; Uterine Diseases
PubMed: 33407588
DOI: 10.1186/s12967-020-02660-x -
International Journal of Clinical... Nov 2022Sinusoidal obstruction syndrome (SOS) refers to liver injury caused by hematopoietic stem cell transplantation (HSCT) and anticancer drugs including oxaliplatin.... (Observational Study)
Observational Study
BACKGROUND
Sinusoidal obstruction syndrome (SOS) refers to liver injury caused by hematopoietic stem cell transplantation (HSCT) and anticancer drugs including oxaliplatin. Increased splenic volume (SV) on computed tomography (CT) indicates oxaliplatin-induced SOS. Similarly, ultrasonography and liver stiffness measurement (LSM) by shear-wave elastography (SWE) can help diagnose SOS after HSCT; however, their usefulness for diagnosing oxaliplatin-induced SOS remains unclear. We investigated the usefulness of the Hokkaido ultrasonography-based scoring system with 10 ultrasonographic parameters (HokUS-10) and SWE in diagnosing oxaliplatin-induced SOS early.
METHODS
In this prospective observational study, ultrasonography and SWE were performed before and at 2, 4, and 6 months after oxaliplatin-based chemotherapy. HokUS-10 was used for assessment. CT volumetry of the SV was performed in clinical practice, and an SV increase ≥ 30% was considered the diagnostic indicator of oxaliplatin-induced SOS. We assessed whether HokUS-10 and SWE can lead to an early detection of oxaliplatin-induced SOS before an increased SV on CT.
RESULTS
Of the 30 enrolled patients with gastrointestinal cancers, 12 (40.0%) with an SV increase ≥ 30% on CT were diagnosed with SOS. The HokUS-10 score was not correlated with an SV increase ≥ 30% (r = 0.18). The change in rate of three HokUS-10 parameters were correlated with an SV increase ≥ 30% (r = 0.32-0.41). The change in rate of LSM by SWE was correlated with an SV increase ≥ 30% (r = 0.40).
CONCLUSIONS
The usefulness of HokUS-10 score was not demonstrated; however, some HokUS-10 parameters and SWE could be useful for the early diagnosis of oxaliplatin-induced SOS.
Topics: Humans; Hepatic Veno-Occlusive Disease; Oxaliplatin; Elasticity Imaging Techniques; Ultrasonography; Antineoplastic Agents
PubMed: 36042137
DOI: 10.1007/s10147-022-02235-4 -
Journal of Clinical Pathology Nov 2021An increasing number of small pulmonary nodules are being screened by CT, and an intraoperative diagnosis is necessary for preventing excessive treatment. However, there...
AIMS
An increasing number of small pulmonary nodules are being screened by CT, and an intraoperative diagnosis is necessary for preventing excessive treatment. However, there is limited literature on the frozen diagnosis of small sclerosing pneumocytomas (SPs). In particular, tumours smaller than 1 cm are challenging for pathologists performing intraoperative frozen diagnosis.
METHODS
In total, 230 cases of SP were surgically resected between January 2015 and March 2019 at Shanghai Chest Hospital, and of them, 76 cases were smaller than 1 cm. The histology and clinical information of these 76 cases (33.0%, 76/230) were reviewed retrospectively, 54 cases of which were diagnosed intraoperatively, and the pitfalls were summarised. All diagnoses were confirmed on permanent sections and immunohistochemical sections.
RESULTS
Histologically, 78.9% (60/76) of the small SP was dominated by one growth pattern, and solid and papillary growth pattern were the most commonly misdiagnosed circumstances. The rate of intraoperative misdiagnosis of these SP smaller than 1 cm was 11.1% (6/54).
CONCLUSIONS
The main reason for misdiagnosis was failure to recognise the dual cell populations and the cellular atypia. Diagnostic clues include the gross morphology, the presence of dual-cell populations and a hypercellular papillary core, foam cell accumulation in glandular spaces and haemorrhage and haemosiderin on the periphery. In spite of awareness of pitfalls some cases may still be essentially impossible to diagnose on frozen section.
Topics: Adult; Aged; Cytodiagnosis; Diagnosis, Differential; Diagnostic Errors; Female; Frozen Sections; Humans; Intraoperative Period; Lung Neoplasms; Male; Middle Aged; Multiple Pulmonary Nodules; Retrospective Studies; Sclerosis; Sensitivity and Specificity; Solitary Pulmonary Nodule
PubMed: 33782195
DOI: 10.1136/jclinpath-2020-206729 -
European Radiology Aug 2022To evaluate the feasibility and accuracy of diagnosing acute heart failure (HF) with CT pulmonary angiography (CTPA) in emergency department patients.
OBJECTIVES
To evaluate the feasibility and accuracy of diagnosing acute heart failure (HF) with CT pulmonary angiography (CTPA) in emergency department patients.
METHODS
In this retrospective single-center study, we evaluated 150 emergency department patients (mean age 65 ± 17 years) undergoing CTPA with a fixed scan (100 kVp) and contrast media protocol (60 mL, 4 mL/s) who had no pulmonary embolism (PE). Patients were subdivided into training cohort (n = 100) and test cohort (n = 50). Three independent, blinded readers measured the attenuation in the right ventricle (RV) and left ventricle (LV) on axial images. The ratio (HU) and difference (HU) between RV and LV attenuation were calculated. Diagnosis of acute HF was made on the basis of clinical, laboratory, and echocardiography data. Optimal thresholds, sensitivity, and specificity were calculated using the area under the curve (AUC) from receiver operating characteristics analysis.
RESULTS
Fifty-nine of the 150 patients (40%) were diagnosed with acute HF. Attenuation measurements showed an almost perfect interobserver agreement (intraclass correlation coefficient: 0.986, 95%CI: 0.980-0.991). NT-pro BNP exhibited moderate correlations with HU (r = 0.50, p < 0.001) and HU (r = 0.50, p < 0.001). In the training cohort, HU (AUC: 0.89, 95%CI: 0.82-0.95) and HU (AUC: 0.88, 95%CI: 0.81-0.95) showed a very good performance to diagnose HF. Optimal cutoff values were 1.42 for HU (sensitivity 93%; specificity 75%) and 113 for HU (sensitivity 93%; specificity 73%). Applying these thresholds to the test cohort yielded a sensitivity of 89% and 89% and a specificity of 69% and 63% for HU and HU, respectively.
CONCLUSION
In emergency department patients undergoing CTPA and showing no PE, both HU and HU have a high sensitivity for diagnosing acute HF.
KEY POINTS
• Heart failure is a common differential diagnosis in patients undergoing CT pulmonary angiography. • In emergency department patients undergoing CT pulmonary angiography and showing no pulmonary embolism, attenuation differences of the left and right ventricle have a high sensitivity for diagnosing acute heart failure.
Topics: Aged; Aged, 80 and over; Angiography; Computed Tomography Angiography; Feasibility Studies; Heart Failure; Humans; Middle Aged; Pulmonary Embolism; Retrospective Studies; Sensitivity and Specificity; Tomography, X-Ray Computed
PubMed: 35294585
DOI: 10.1007/s00330-022-08676-9 -
International Journal of Infectious... Jul 2020Diagnosing pulmonary blastomycosis (PB) requires the detection of Blastomyces dermatitidis in pulmonary secretions or tissue, which can be achieved via bronchoscopic...
OBJECTIVES
Diagnosing pulmonary blastomycosis (PB) requires the detection of Blastomyces dermatitidis in pulmonary secretions or tissue, which can be achieved via bronchoscopic procedures like bronchoalveolar lavage (BAL) or brush and transbronchial biopsy (TBBx). This descriptive study retrieved the data of PB that was diagnosed by bronchoscopy to define which bronchoscopic procedure produced the highest yield.
METHODS
Retrospectively, all patients diagnosed with PB via bronchoscopic approach were identified. Non-invasive BAL was referred to when performed first in the order of bronchoscopic procedures, and invasive BAL was used when it was performed after other bronchoscopic procedures.
RESULTS
A total of 111 patients were included in the study. BAL produced the highest yield of all bronchoscopic procedures (>87%), regardless if it was performed first in order (non-invasive, 87.3%) or not (invasive BAL, 89.6%) (p = 0.43). Performing bronchoscopy and BAL earlier in the course of the disease resulted in a significantly better diagnostic yield.
CONCLUSIONS
BAL is probably enough to diagnose PB. Also, it had the best yield when performed earlier, regardless of whether it was performed first in order or not. BAL culture had a better yield in detecting Blastomyces dermatitidis over fungal stain and cytology.
Topics: Adolescent; Adult; Aged; Aged, 80 and over; Blastomycosis; Bronchoalveolar Lavage; Bronchoscopy; Female; Humans; Male; Middle Aged; Retrospective Studies; Young Adult
PubMed: 32371194
DOI: 10.1016/j.ijid.2020.04.077 -
Statistical Methods in Medical Research Aug 2022A cancer diagnosis is part of a complex stochastic process, which involves patient's characteristics, diagnosing methods, an initial assessment of cancer progression,...
A cancer diagnosis is part of a complex stochastic process, which involves patient's characteristics, diagnosing methods, an initial assessment of cancer progression, treatments and a certain outcome of interest. To evaluate the performance of diagnoses, one needs not only a consistent estimation of the causal effect under a specified regime of diagnoses and treatments but also reliable confidence interval, -value and hypothesis testing of the causal effect. In this article, we identify causal effects under various regimes of diagnoses and treatments by the point effects of diagnoses and treatments and thus are able to estimate and test these causal effects by estimating and testing point effects in the familiar framework of single-point causal inference. Specifically, using data from a Swedish prognosis study of stomach cancer, we estimate and test the causal effects on cancer survival under various regimes of diagnosing and treating hospitals including the optimal regime. We also estimate and test the modification of the causal effect by age. With its simple setting, one can readily extend the example to a large variety of settings in the area of cancer diagnosis: different personal characteristics such as family history, different diagnosing procedures such as multistage screening, and different cancer outcomes such as cancer progression.
Topics: Causality; Early Detection of Cancer; Humans; Neoplasms; Prognosis; Research Design; Treatment Outcome
PubMed: 35509212
DOI: 10.1177/09622802221098429 -
Cancer Medicine Aug 2023Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic...
BACKGROUND AND AIMS
Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens.
METHODS
HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model.
RESULTS
A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.
CONCLUSIONS
An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.
Topics: Humans; Endoscopic Ultrasound-Guided Fine Needle Aspiration; Cytology; Deep Learning; Pancreatic Neoplasms; Carcinoma, Pancreatic Ductal
PubMed: 37455599
DOI: 10.1002/cam4.6335 -
International Journal of Environmental... Jul 2021Human performance optimization of tactical personnel requires accurate, meticulous, and effective monitoring of biological adaptations and systemic recovery. Due to an... (Review)
Review
Human performance optimization of tactical personnel requires accurate, meticulous, and effective monitoring of biological adaptations and systemic recovery. Due to an increased understanding of its importance and the commercial availability of assessment tools, the use of heart rate variability (HRV) to address this need is becoming more common in the tactical community. Measuring HRV is a non-invasive, practical method for objectively assessing a performer's readiness, workload, and recovery status; when combined with additional data sources and practitioner input, it provides an affordable and scalable solution for gaining actionable information to support the facilitation and maintenance of operational performance. This narrative review discusses the non-clinical use of HRV for assessing, monitoring, and interpreting autonomic nervous system resource availability, modulation, effectiveness, and efficiency in tactical populations. Broadly, HRV metrics represent a complex series of interactions resulting from internal and external stimuli; therefore, a general overview of HRV applications in tactical personnel is discussed, including the influence of occupational specific demands, interactions between cognitive and physical domains, and recommendations on implementing HRV for training and recovery insights into critical health and performance outcomes.
Topics: Autonomic Nervous System; Heart Rate; Humans; Monitoring, Physiologic; Workload
PubMed: 34360435
DOI: 10.3390/ijerph18158143