INTRODUCTION: This study aimed to examine patterns of diagnosis, cognitive and adaptive functioning, and school placement outcomes in autistic children in Singapore, and to assess earlier predictive factors of cognitive outcomes. METHOD: Retrospective data were extracted from medical records of a specialist developmental paediatrics service for children born in 2008-2011 and referred to the autism clinic or were given a diagnosis of autism. Data items included demographic data, diagnostic methods, psychological assessment results, early intervention attendance and school placement outcomes. RESULTS: A total of 2124 children (82.6% male; 66.4% Chinese, 13.4% Malay, 9.8% Indian and 10.5% Others) were diagnosed with autism from the 4 birth-year cohorts. The mean (SD) age of the first clinical diagnosis of autism was 3.56 (1.14) years, with 81.0% of children receiving a concordant initial clinical diagnosis. A total of 1811 (85.2%) had a formal diagnostic assessment using the Autism Diagnostic Observation Schedule (ADOS) at a mean (SD) age of 4.16 (1.11) years. Of 1326 with cognitive and adaptive assessment results, 16.6% had mild and 19.8% had moderate-severe cognitive impairment. Of 1483 with school placement outcomes, 45.9% went to mainstream schools, 21.8% entered SPED schools offering the national curriculum and 32.3% required customised curriculum SPED schools. Logistic regression showed that factors predicting intellectual impairment included higher ADOS scores (aOR 95% CI 1.13 [1.08-1.19] for Comm+SI total and 1.53 [1.33-1.75] for SBRI total), higher social communication level of support (based on the DSM-5 criteria) (aOR [95% CI] 2.14 [1.10-4.16] for level 2 and 14.94 [5.77-38.64] for level 3), and minority race (aOR [95% CI] 2.82 [1.52-5.20] for Malay, 5.19 [2.36-11.44] for Indian, and 4.54 [1.91-10.79] for Others). CONCLUSION These findings could guide policymakers and practitioners worldwide to strategically allocate diagnostic, intervention and educational resources, maximising developmental outcomes for autistic children across diverse settings.
Humans ; Singapore/epidemiology* ; Male ; Female ; Retrospective Studies ; Child, Preschool ; Autistic Disorder/complications* ; Child ; Early Intervention, Educational ; Cognitive Dysfunction/diagnosis* ; Cognition

INTRODUCTION: The high prevalence and mortality rates of breast cancer and lung cancer in Singapore necessitate robust screening programmes to enable early detection and intervention for improved patient outcomes, yet 2024 uptake and coverage remain suboptimal. This narrative review synthesises expert perspectives from a recent roundtable discussion and proposes strategies to advance breast cancer and lung cancer screening programmes. METHOD: A 2024 roundtable convened clinical practitioners, health policymakers, researchers and patient advocates discussed current challenges and opportunities for improving cancer screening in Singapore. Perspectives and insights were analysed to identify themes related to existing programme gaps, opportunities for innovation and implementation challenges. DISCUSSION: Singapore's national breast cancer screening programme has been in place for over 2 decades, yet screening uptake remains suboptimal. A national lung cancer screening programme, in contrast, is still in its early stages of implementation. Regardless, employment of risk stratification approaches that integrate genetic, demographic and lifestyle factors could enhance screening effectiveness by identifying high-risk indivi-duals, while also taking local epidemiological trends into consideration. Integration of digital health technologies, artificial intelligence and behavioural change models can enhance cancer screening uptake and accuracy to overcome barriers such as low awareness, cultural beliefs and socioeconomic factors that contribute to low participation rates. CONCLUSION Key recommendations include enhancing public awareness, refining screening guidelines, expanding access and applying innovative technologies. A coordinated effort among stakeholders is crucial to continually assess and enhance screening programmes to narrow the practice-policy gap and ultimately reduce breast cancer and lung cancer burden in Singapore.
Humans ; Singapore/epidemiology* ; Lung Neoplasms/epidemiology* ; Breast Neoplasms/epidemiology* ; Early Detection of Cancer/methods* ; Female ; Mass Screening/organization & administration*

INTRODUCTION: Risk-based screening (RBS) has emerged as a promising alternative to age-based cancer screening. However, evidence regarding real-world implementation outcomes remains fragmented. Thus, a systematic review was conducted to evaluate the implementation metho-dologies and outcomes of RBS programmes across different cancer types. METHODS: MEDLINE, Embase, CINAHL, Web of Science, Cochrane Central Register of Controlled Trials and Scopus were systematically searched from their respective dates of inception up to 8 July 2024. Prospective and rando-mised controlled trials (RCTs), which implement the RBS of cancer in an asymptomatic population, or studies retrospectively evaluating the outcomes of the same were included. Geographic distribution, population characteristics, RBS methodology, diagnostic accuracy and clinical outcomes were narratively synthesised. RESULTS: Among the 33 included studies (i.e. 21 prospective cohort, 8 RCTs, 3 retrospective and 1 non-RCT), sample sizes ranged from 102 to 1,429,890 participants. Most RBS trials were conducted in China (n=7, 21.2%), followed by the Netherlands (n=4, 12.1%) then the US, Australia and Sweden (n=3, 9.8%). Studies predominantly examined colorectal (27.3%), breast (21.2%) and prostate cancer (18.2%). Three main stratification approaches emerged: algorithmic (48.5%), validated risk models (39.4%) and physician assessment (9.1%). Implementation outcomes showed higher uptake in moderate-risk (75.4%) compared to high-risk (71.3%) and low-risk groups (67.9%). Five studies demonstrated cost-effectiveness with increased quality-adjusted life years, while 12 studies showed superior or non-inferior cancer detection rates compared to traditional screening. CONCLUSION The RBS of cancer has the potential to optimise healthcare resource allocation while minimising harm and increasing receptiveness for patients. More work is needed to evaluate long-term outcomes prior to the scaling of RBS programmes.
Humans ; Early Detection of Cancer/methods* ; Neoplasms/diagnosis* ; Risk Assessment ; Mass Screening/methods*

BACKGROUND: Most studies have evaluated disability-adjusted life years (DALYs) of colorectal cancer (CRC) patients based on a set of generic disability weights (DWs). This study aimed to apply local CRC-stage-specific DWs to estimate the burden of DALYs for CRC (CRC-DALYs) in populations in China and consider the influence of local screening coverage of CRC. METHODS: A prevalence-based model was constructed using data from various sources. Years lived with disability (YLDs) were estimated mainly via cumulative prevalence data (based on CRC incidence rates, population numbers, and survival rates), stage-specific proportions of CRC, and DWs of the local population. Years of life lost (YLLs) were calculated based on the CRC mortality rates and standard life expectancies. CRC incidence and mortality rates for the years 2020, 2025, and 2030 were estimated by joinpoint regression, and the corresponding DALYs were predicted. The main assumption was made for CRC screening coverage. Sensitivity analyses were used to assess the impact of population, DWs, and coverage. RESULTS: In 2017, among the Chinese population, the estimated number of CRC-DALYs was 4,303,314 (11.9% for YLDs). If CRC screening coverage rate in China (2.3%) remains unchanged, the overall DALYs in 2030 are predicted to increase by 37.2% (45.1% of those aged ≥65 years). More optimistically, the DALYs would then decrease by 0.7% in 2030 (from 5,902,454 to 5,860,200) if the coverage could be increased to 25.0%. A sensitivity analysis revealed that using local DWs would change the base-case values by 5.7%. CONCLUSIONS The estimated CRC-DALYs in China using population-specific DWs were considerably lower (with a higher percentage of YLDs) than the global burden of disease (GBD) estimates (5,865,004, of 4.6% for YLDs), suggesting the impact extent of applying local parameters. Sustainable scale-up CRC screening needs to be in place to moderate the growth trend of CRC-DALYs in China.
Humans ; Colorectal Neoplasms/diagnosis* ; China/epidemiology* ; Disability-Adjusted Life Years ; Male ; Prevalence ; Female ; Middle Aged ; Aged ; Early Detection of Cancer ; Quality-Adjusted Life Years ; Adult ; Incidence

Humans ; Language Development Disorders/diagnosis* ; Early Diagnosis ; Child

Humans ; Bronchiolitis Obliterans/etiology* ; Early Diagnosis ; Pneumonia, Mycoplasma/complications* ; Consensus ; Child ; Mycoplasma pneumoniae ; China/epidemiology* ; Mass Screening ; Adenoviridae Infections/complications*

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

Colorectal polyps are important early markers of colorectal cancer, and their early detection is crucial for cancer prevention. Although existing polyp segmentation models have achieved certain results, they still face challenges such as diverse polyp morphology, blurred boundaries, and insufficient feature extraction. To address these issues, this study proposes a parallel coordinate fusion network (PCFNet), aiming to improve the accuracy and robustness of polyp segmentation. PCFNet integrates parallel convolutional modules and a coordinate attention mechanism, enabling the preservation of global feature information while precisely capturing detailed features, thereby effectively segmenting polyps with complex boundaries. Experimental results on Kvasir-SEG and CVC-ClinicDB demonstrate the outstanding performance of PCFNet across multiple metrics. Specifically, on the Kvasir-SEG dataset, PCFNet achieved an F1-score of 0.897 4 and a mean intersection over union (mIoU) of 0.835 8; on the CVC-ClinicDB dataset, it attained an F1-score of 0.939 8 and an mIoU of 0.892 3. Compared with other methods, PCFNet shows significant improvements across all performance metrics, particularly in multi-scale feature fusion and spatial information capture, demonstrating its innovativeness. The proposed method provides a more reliable AI-assisted diagnostic tool for early colorectal cancer screening.
Humans ; Colonic Polyps/diagnostic imaging* ; Colorectal Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Early Detection of Cancer

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
Humans ; Lung Neoplasms/diagnosis* ; Artificial Intelligence ; Early Detection of Cancer/methods* ; Precision Medicine ; Image Processing, Computer-Assisted/methods* ; Tomography, X-Ray Computed ; Radiomics

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning