BACKGROUND

Down syndrome or trisomy 21, the most common chromosomal disorder, results from the presence of a third copy of chromosome 21 and manifests as mild to moderate intellectual disability, growth retardation, congenital heart defects, gastrointestinal abnormalities, and characteristic facial features. Several methods have been used to screen for Down syndrome in the prenatal period, such as ultrasound, biomarkers, cell-free DNA testing, and combinations of these tests. A positive result from one or more of these screening tests signals the need for confirmatory karyotyping to clinch the diagnosis. Ultrasound between 11 to 14 weeks of gestation can evaluate nuchal translucency (NT) to screen for Down syndrome. During the second trimester, a triple or quadruple test can also be performed alone or in addition to NT to quantify Down syndrome risk. In limited resource settings however, only the measurement of NT via ultrasound can be performed since biomarker tests are either unavailable or inaccessible. While the diagnostic performance of NT measurement alone has been investigated in several observational studies, there is no consensus on its performance as a sole test to screen for Down syndrome.

To determine the diagnostic performance of NT during prenatal first-trimester ultrasound as a screening test for Down syndrome.

We performed a systematic search on the PubMed, ProQuest, and Cochrane Library databases for recent systematic reviews and meta-analyses that addressed the objective. The existing reviews found were then independently appraised by the two reviewers with the AMSTAR-2 checklist. To update the existing reviews, a systematic search was done in the same databases to identify additional primary diagnostic studies, which were appraised using the QUADAS-2 tool. Random-effects univariate meta-analysis and summary receiving operator curve (HSROC) analysis for the outcomes were performed using Review Manager version 5.4 and R version 4.2.2, respectively. Subgroup analysis was performed by stratifying the baseline risk of mothers for fetal anomaly as low- or high-risk. Highrisk mothers were defined as women with risk factors such as advanced age, positive serum screen, presence of other ultrasound anomalies, and history of previous fetus with anomaly.

We found 22 cohort studies (n=225,846) of women at low-risk for fetal anomaly. The pooled sensitivity was 67.8% (95% CI: 61.4%-73.6%, I2=70.4%) and specificity was 96.3% (95% CI: 95.5%-96.9%, I2=96.7%). For low-risk women, the overall certainty of evidence was low, due to different modes of verification and heterogeneity not completely explained by variability in baseline risk or cut-points. Seven studies (n=9,197) were on high-risk women. The pooled sensitivity was 62.2% (95% CI: 54.1%-69.7%, I2=38.8%) and specificity was 96.5% (95% CI: 93.6%-98.1%, I2=95.5%). For women at high-risk, the evidence was rated as moderate due to differential verification.

Our analysis showed that NT measured through first-trimester ultrasound is specific for Down syndrome but has low sensitivity. Despite this, it is a useful screening test for Down syndrome in low-resource settings where other strategies may not be available or accessible. Furthermore, interpretation of NT results must take into consideration its limited sensitivity as this may lead to missed cases.

BACKGROUND

Down syndrome or trisomy 21, the most common chromosomal disorder, results from the presence of a third copy of chromosome 21 and manifests as mild to moderate intellectual disability, growth retardation, congenital heart defects, gastrointestinal abnormalities, and characteristic facial features. Several methods have been used to screen for Down syndrome in the prenatal period, such as ultrasound, biomarkers, cell-free DNA testing, and combinations of these tests. A positive result from one or more of these screening tests signals the need for confirmatory karyotyping to clinch the diagnosis. Ultrasound between 11 to 14 weeks of gestation can evaluate nuchal translucency (NT) to screen for Down syndrome. During the second trimester, a triple or quadruple test can also be performed alone or in addition to NT to quantify Down syndrome risk. In limited resource settings however, only the measurement of NT via ultrasound can be performed since biomarker tests are either unavailable or inaccessible. While the diagnostic performance of NT measurement alone has been investigated in several observational studies, there is no consensus on its performance as a sole test to screen for Down syndrome.

To determine the diagnostic performance of NT during prenatal first-trimester ultrasound as a screening test for Down syndrome.

We performed a systematic search on the PubMed, ProQuest, and Cochrane Library databases for recent systematic reviews and meta-analyses that addressed the objective. The existing reviews found were then independently appraised by the two reviewers with the AMSTAR-2 checklist. To update the existing reviews, a systematic search was done in the same databases to identify additional primary diagnostic studies, which were appraised using the QUADAS-2 tool. Random-effects univariate meta-analysis and summary receiving operator curve (HSROC) analysis for the outcomes were performed using Review Manager version 5.4 and R version 4.2.2, respectively. Subgroup analysis was performed by stratifying the baseline risk of mothers for fetal anomaly as low- or high-risk. Highrisk mothers were defined as women with risk factors such as advanced age, positive serum screen, presence of other ultrasound anomalies, and history of previous fetus with anomaly.

We found 22 cohort studies (n=225,846) of women at low-risk for fetal anomaly. The pooled sensitivity was 67.8% (95% CI: 61.4%-73.6%, I2=70.4%) and specificity was 96.3% (95% CI: 95.5%-96.9%, I2=96.7%). For low-risk women, the overall certainty of evidence was low, due to different modes of verification and heterogeneity not completely explained by variability in baseline risk or cut-points. Seven studies (n=9,197) were on high-risk women. The pooled sensitivity was 62.2% (95% CI: 54.1%-69.7%, I2=38.8%) and specificity was 96.5% (95% CI: 93.6%-98.1%, I2=95.5%). For women at high-risk, the evidence was rated as moderate due to differential verification.

Our analysis showed that NT measured through first-trimester ultrasound is specific for Down syndrome but has low sensitivity. Despite this, it is a useful screening test for Down syndrome in low-resource settings where other strategies may not be available or accessible. Furthermore, interpretation of NT results must take into consideration its limited sensitivity as this may lead to missed cases.

BACKGROUND: Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer. METHODS: MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test. RESULTS: A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies. CONCLUSIONS Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.
Humans ; Adult ; Middle Aged ; Aged ; Lung Neoplasms/diagnostic imaging* ; Early Detection of Cancer ; Sensitivity and Specificity ; Mass Screening ; Tomography, X-Ray Computed

Humans ; Biopsy, Fine-Needle ; Consensus ; Thyroid Neoplasms/pathology* ; Thyroid Nodule/pathology* ; Sensitivity and Specificity

OBJECTIVE: To develop a few-shot learning (FSL) approach for classifying optical coherence tomography (OCT) images in patients with inherited retinal disorders (IRDs). METHODS: In this study, an FSL model based on a student-teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples. RESULTS: The FSL model achieved a total accuracy of 0.974-0.983, total sensitivity of 0.934-0.957, total specificity of 0.984-0.990, and total F1 score of 0.935-0.957, which were superior to the total accuracy of the baseline model of 0.943-0.954, total sensitivity of 0.866-0.886, total specificity of 0.962-0.971, and total F1 score of 0.859-0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves (AUC) of the receiver operating characteristic (ROC) curves in most subclassifications. CONCLUSION This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.
Humans ; Tomography, Optical Coherence ; Deep Learning ; Retinal Diseases/diagnostic imaging* ; Retina/diagnostic imaging* ; ROC Curve

Humans ; Pregnancy ; Female ; Culture Media ; Pregnancy Complications, Infectious ; Sensitivity and Specificity

OBJECTIVES: This study aims to predict the risk of deep caries exposure in radiographic images based on the convolutional neural network model, compare the prediction results of the network model with those of senior dentists, evaluate the performance of the model for teaching and training stomatological students and young dentists, and assist dentists to clarify treatment plans and conduct good doctor-patient communication before surgery. METHODS: A total of 206 cases of pulpitis caused by deep caries were selected from the Department of Stomatological Hospital of Tianjin Medical University from 2019 to 2022. According to the inclusion and exclusion criteria, 104 cases of pulpitis were exposed during the decaying preparation period and 102 cases of pulpitis were not exposed. The 206 radiographic images collected were randomly divided into three groups according to the proportion: 126 radiographic images in the training set, 40 radiographic images in the validation set, and 40 radiographic images in the test set. Three convolutional neural networks, visual geometry group network (VGG), residual network (ResNet), and dense convolutional network (DenseNet) were selected to analyze the rules of the radiographic images in the training set. The radiographic images of the validation set were used to adjust the super parameters of the network. Finally, 40 radiographic images of the test set were used to evaluate the performance of the three network models. A senior dentist specializing in dental pulp was selected to predict whether the deep caries of 40 radiographic images in the test set were exposed. The gold standard is whether the pulp is exposed after decaying the prepared hole during the clinical operation. The prediction effect of the three network models (VGG, ResNet, and DenseNet) and the senior dentist on the pulp exposure of 40 radiographic images in the test set were compared using receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score to select the best network model. RESULTS: The best network model was DenseNet model, with AUC of 0.97. The AUC values of the ResNet model, VGG model, and the senior dentist were 0.89, 0.78, and 0.87, respectively. Accuracy was not statistically different between the senior dentist (0.850) and the DenseNet model (0.850)(P>0.05). Kappa consistency test showed moderate reliability (Kappa=0.6>0.4, P<0.05). CONCLUSIONS Among the three convolutional neural network models, the DenseNet model has the best predictive effect on whether deep caries are exposed in imaging. The predictive effect of this model is equivalent to the level of senior dentists specializing in dental pulp.
Humans ; Deep Learning ; Neural Networks, Computer ; Pulpitis/diagnostic imaging* ; Reproducibility of Results ; ROC Curve ; Random Allocation

Objective To explore the optimal parameters for virtual mono-energetic imaging of liver solid lesions. Methods A retrospective analysis was performed on 60 patients undergoing contrast-enhanced spectral CT of the abdomen.The iodine concentration values of hepatic arterial phase images and the CT values of different mono-energetic images were measured.The correlation coefficient and coefficient of variation were calculated. Results The average correlation coefficients between iodine concentrations and CT values of hepatic solid lesion images at 40,45,50,55,60,65,and 70 keV were 0.996,0.995,0.993,0.989,0.978,0.970,and 0.961,respectively.The correlation coefficients at 40(P=0.007),45(P=0.022),50 keV (P=0.035)were higher than that at 55 keV,and the correlation coefficients at 40 keV(P=0.134) and 45 keV(P=0.368) had no significant differences from that at 50 keV.The coefficients of variation of the CT values at 40,45,and 50 keV were 0.146,0.154,and 0.163,respectively. Conclusion The energy of 40 keV is optimal for virtual mono-energetic imaging of liver solid lesions in the late arterial phase,which is helpful for the diagnosis of liver diseases.
Humans ; Tomography, X-Ray Computed ; Retrospective Studies ; Abdomen ; Iodine ; Liver/diagnostic imaging* ; Signal-To-Noise Ratio ; Radiographic Image Interpretation, Computer-Assisted/methods*

Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.
Humans ; Computed Tomography Angiography/methods* ; Radiation Dosage ; Deep Learning ; Radiographic Image Interpretation, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

Objective To investigate the clinical value of high-frequency ultrasound in the diagnosis of pronator teres syndrome (PTS). Methods The high-frequency ultrasound was employed to examine and measure the median nerve of the pronator teres muscle in 30 patients with PTS and 30 healthy volunteers (control group).The long-axis diameter (LA),short-axis diameter (SA) and cross-sectional area (CSA) of the median nerve were measured.The receiver operating characteristic curve of the median nerve ultrasonic measurement results was established,and the area under the curve (AUC) was calculated.The diagnostic efficiency of each index for PTS was compared with the surgical results as a reference. Results The PTS group showed larger LA[(5.02±0.50) mm vs.(3.89±0.41) mm;t=4.38,P=0.013],SA[(2.55±0.46) mm vs.(1.70±0.41) mm;t=5.19,P=0.009],and CSA[(11.13±3.72) mm² vs.(6.88±2.68) mm²;t=8.42,P=0.008] of the median nerve than the control group.The AUC of CSA,SA,and LA was 94.3% (95%CI=0.912-0.972,Z=3.586,P=0.001),77.7% (95%CI=0.734-0.815,Z=2.855, P=0.006),and 78.8% (95%CI=0.752-0.821,Z=3.091,P=0.004),respectively.With 8.63 mm² as the cutoff value,the sensitivity and specificity of CSA in diagnosing PTS were 93.3% and 90.0%,respectively. Conclusion High-frequency ultrasound is a practical method for diagnosing PTS,and the CSA of median nerve has a high diagnostic value.
Humans ; Forearm/innervation* ; Muscle, Skeletal/innervation* ; Median Nerve/diagnostic imaging* ; Ultrasonography/methods* ; Sensitivity and Specificity