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.

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography

INTRODUCTION: Radiology plays an integral role in fracture detection in the emergency department (ED). After hours, when there are fewer reporting radiologists, most radiographs are interpreted by ED physicians. A minority of these interpretations may miss diagnoses, which later require the callback of patients for further management. Artificial intelligence (AI) has been viewed as a potential solution to augment the shortage of radiologists after hours. We explored the efficacy of an AI solution in the detection of appendicular and pelvic fractures for adult radiographs performed after hours at a general hospital ED in Singapore, and estimated the potential monetary and non-monetary benefits. METHODS: One hundred and fifty anonymised abnormal radiographs were retrospectively collected and fed through an AI fracture detection solution. The radiographs were re-read by two radiologist reviewers and their consensus was established as the reference standard. Cases were stratified based on the concordance between the AI solution and the reviewers' findings. Discordant cases were further analysed based on the nature of the discrepancy into overcall and undercall subgroups. Statistical analysis was performed to evaluate the accuracy, sensitivity and inter-rater reliability of the AI solution. RESULTS: Ninety-two examinations were included in the final study radiograph set. The AI solution had a sensitivity of 98.9%, an accuracy of 85.9% and an almost perfect agreement with the reference standard. CONCLUSION An AI fracture detection solution has similar sensitivity to human radiologists in the detection of fractures on ED appendicular and pelvic radiographs. Its implementation offers significant potential measurable cost, manpower and time savings.
Humans ; Singapore ; Emergency Service, Hospital ; Fractures, Bone/diagnostic imaging* ; Artificial Intelligence ; Retrospective Studies ; Adult ; Male ; Female ; Cost Savings ; Middle Aged ; Pelvic Bones/diagnostic imaging* ; Reproducibility of Results ; Aged ; Sensitivity and Specificity ; Radiography

INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency department (ED) physicians on a binomial classification task. METHODS: A total of 1,314 paediatric elbow lateral radiographs (patient mean age 8.2 years) were retrospectively retrieved and classified based on annotation as normal or abnormal (with pathology). They were then randomly partitioned to a development set (993 images); first and second tuning (validation) sets (109 and 100 images, respectively); and a test set (112 images). An artificial intelligence (AI) model was trained on the development set using the EfficientNet B1 network architecture. Its performance on the test set was compared to that of five physicians (inter-rater agreement: fair). Performance of the AI model and the physician group was tested using McNemar test. RESULTS: The accuracy of the AI model on the test set was 80.4% (95% confidence interval [CI] 71.8%-87.3%), and the area under the receiver operating characteristic curve (AUROC) was 0.872 (95% CI 0.831-0.947). The performance of the AI model vs. the physician group on the test set was: sensitivity 79.0% (95% CI: 68.4%-89.5%) vs. 64.9% (95% CI: 52.5%-77.3%; P = 0.088); and specificity 81.8% (95% CI: 71.6%-92.0%) vs. 87.3% (95% CI: 78.5%-96.1%; P = 0.439). CONCLUSION The AI model showed good AUROC values and higher sensitivity, with the P-value at nominal significance when compared to the clinician group.
Humans ; Deep Learning ; Child ; Retrospective Studies ; Male ; Female ; Radiography/methods* ; ROC Curve ; Elbow/diagnostic imaging* ; Neural Networks, Computer ; Child, Preschool ; Elbow Joint/diagnostic imaging* ; Emergency Service, Hospital ; Adolescent ; Infant ; Artificial Intelligence

INTRODUCTION: The diagnosis of sarcopenia relies on key indicators such as handgrip strength, walking speed and muscle mass. Developing a composite index that integrates these measures could enhance clinical evaluation in older adults. This study aimed to standardise and combine these metrics to establish a z score for the sarcopenia composite index (ZoSCI) tailored for the ageing population. Additionally, we explore the risk factors associated with ZoSCI to provide insights into early prevention and intervention strategies. METHOD: This retrospective study analysed data between January 2017 and December 2021 from an elderly health programme in Taiwan, applying the Asian Working Group for Sarcopenia criteria to assess sarcopenia. ZoSCI was developed by standardising handgrip strength, walking speed and muscle mass into z scores and integrating them into a composite index. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values, and multiple regression analysis identified factors influencing ZoSCI. RESULTS: Among the 5047 participants, the prevalence of sarcopenia was 3.7%, lower than the reported global prevalence of 3.9-15.4%. ROC curve analysis established optimal cut-off points for distinguishing sarcopenia in ZoSCI: -1.85 (sensitivity 0.91, specificity 0.88) for males and -1.97 (sensitivity 0.93, specificity 0.88) for females. Factors associated with lower ZoSCI included advanced age, lower education levels, reduced exercise frequency, lower body mass index and creatinine levels. CONCLUSION This study introduces ZoSCI, a new compo-site quantitative indicator for identifying sarcopenia in older adults. The findings highlight specific risk factors that can inform early intervention. Future studies should validate ZoSCI globally, with international collaborations to ensure broader applicability.
Humans ; Sarcopenia/physiopathology* ; Male ; Aged ; Female ; Retrospective Studies ; Hand Strength ; Taiwan/epidemiology* ; ROC Curve ; Aged, 80 and over ; Risk Factors ; Walking Speed ; Geriatric Assessment/methods* ; Prevalence ; Muscle, Skeletal ; Middle Aged

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction. METHOD: Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI. RESULTS: There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV. CONCLUSION The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
Humans ; Male ; Female ; Emergency Service, Hospital ; Middle Aged ; Electrocardiography ; Machine Learning ; Singapore ; Chest Pain/blood* ; Troponin I/blood* ; Myocardial Infarction/blood* ; Risk Assessment/methods* ; Aged ; Algorithms ; Acute Coronary Syndrome/blood* ; Adult ; Sensitivity and Specificity

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

BACKGROUND: Concurrent chemo-radiotherapy (CCRT) is the standard treatment for locally advanced cervical cancer (LACC), but there are still many patients who suffer tumor recurrence. However, valuable predictors of treatment outcomes remain limited. This study aimed to assess the value of the serum immune biomarkers to predict the prognosis. METHODS: We reviewed cervical cancer patients treated with CCRT between January 2014 and May 2018 at Peking Union Medical College Hospital. The systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and lactate dehydrogenase (LDH) were calculated using blood samples. The relationship between immune markers and the treatment outcome was analyzed. The area under the receiver operating characteristic (ROC) curve was used to evaluate the predictive efficiency. The Cox proportional hazards model and log-rank were used to predict overall survival (OS) and disease-free survival (DFS). RESULTS: This study included 667 patients. Among them, 195 (29.2%) patients were defined as treatment failure, including 127 (19.0%) patients with pelvic failure, 94 (14.1%) distant failure, and 25 (3.7%) concurrent pelvic and distant failure. It revealed that the tumor stage, size, metastatic lymph nodes (MLNs), and serum immune biomarkers, such as SII, SIRI, and LDH, were significantly related to treatment outcomes. We demonstrated that the optimal cut-off of the SII, SIRI, and LDH were 970.4 × 10 9 /L, 1.3 × 10 9 /L, and 207.52 U/L, respectively. Importantly, this study presented that LDH level had the highest OR (OR = 4.2; 95% CI [2.3-10.8]). Furthermore, the OS and DFS for patients with pre-SII ≥970.5 × 10 9 /L were significantly worse than those with pre-SII <970.5 × 10 9 /L. Similarly, pre-SIRI ≥1.25 × 10 9 /L and pre-LDH ≥207.5 U/L were related to poor survival outcomes. CONCLUSIONS This study demonstrated that the baseline SII, SIRI, and LDH levels can be used to accurately and effectively predict the treatment outcomes after CCRT and long-term prognosis. Our results may offer additional prognostic information in clinical, which helps to detect the potential recurrent metastasis in time.
Humans ; Female ; Uterine Cervical Neoplasms/drug therapy* ; Middle Aged ; Adult ; Aged ; Chemoradiotherapy/methods* ; L-Lactate Dehydrogenase/blood* ; Treatment Outcome ; Disease-Free Survival ; Prognosis ; ROC Curve ; Biomarkers, Tumor/blood* ; Proportional Hazards Models

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms