Artificial intelligence assistance for fetal development: evaluation of an automated software for biometry measurements in the mid-trimester.

BACKGROUND: This study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CUPID performance of experienced senior and junior radiologists. MATERIALS AND METHODS: This prospective cross-sectional study was conducted at Shenzhen University General Hospital between September 2022 and June 2023, and focused on mid-trimester fetuses. All ultrasound images of the six standard planes, that enabled the evaluation of nine biometric measurements, were included to compare the performance of CUPID through subjective and objective assessments. RESULTS: There were 642 fetuses with a mean (±SD) age of 22 ± 2.82 weeks at enrollment. In the subjective quality assessment, out of 642 images representing nine biometric measurements, 617-635 images (90.65-96.11%) of CUPID caliper placements were determined to be accurately placed and did not require any adjustments. Whereas, for the junior category, 447-691 images (69.63-92.06%) were determined to be accurately placed and did not require any adjustments. In the objective measurement indicators, across all nine biometric parameters and estimated fetal weight (EFW), the intra-class correlation coefficients (ICC) (0.843-0.990) and Pearson correlation coefficients (PCC) (0.765-0.978) between the senior radiologist and CUPID reflected good reliability compared with the ICC (0.306-0.937) and PCC (0.566-0.947) between the senior and junior radiologists. Additionally, the mean absolute error (MAE), percentage error (PE), and average error in days of gestation were lower between the senior and CUPID compared to the difference between the senior and junior radiologists. The specific differences are as follows: MAE (0.36-2.53 mm, 14.67 g) compared to (0.64- 8.13 mm, 38.05 g), PE (0.94-9.38%) compared to (1.58-16.04%), and average error in days (3.99-7.92 days) compared to (4.35-11.06 days). In the time-consuming task, CUPID only takes 0.05-0.07 s to measure nine biometric parameters, while senior and junior radiologists require 4.79-11.68 s and 4.95-13.44 s, respectively. CONCLUSIONS: CUPID has proven to be highly accurate and efficient software for automatically measuring fetal biometry, gestational age, and fetal weight, providing a precise and fast tool for assessing fetal growth and development.

Establishment and application of a rapid assay for GII.4/GII.17 NoV detection based on the combination of CRISPR/Cas13a and isothermal amplification.

Introduction: Norovirus (NoV) is one of the most important agents responsible for viral acute gastroenteritis, among which GII.4 NoV is the predominant strain worldwide, and GII.17 NoV surpassed GII.4 in some epidemic seasons. Rapid and accurate gene recognition is essential for a timely response to NoV outbreaks. Methods: In the present study, the highly conserved regions of GII.4 and GII.17 NoVs were identified in the junction of open reading frame (ORF) 1 and ORF2 and then amplified by isothermal recombinase-aided amplification (RAA), followed by the cleavage of CRISPR-Cas13a with screened CRISPR RNAs (crRNAs) and RAA primers. The entire detection procedure could be completed within 40 min using a thermostat, and the results could be read out by the naked eye under a portable blue light transilluminator. Discussion: The assay showed a high sensitivity of 97.96% and a high specificity of 100.0%. It offered a low limit of detection (LOD) of 2.5×100 copies/reaction and a coincidence rate of 96.75% in 71 clinical fecal samples. Overall, rapid and inexpensive detection of GII.4/GII.17 NoVs was established, which makes it possible to be used in areas with limited resources, particularly in low-income countries. Furthermore, it will contribute to assessing transmission risks and implementing control measures for GII.4/GII.17 NoVs, making healthcare more accessible worldwide.

An intelligent quantification system for fetal heart rhythm assessment: A multicenter prospective study.

BACKGROUND: The motion relationship and time intervals of the pulsed-wave Doppler (PWD) spectrum are essential for diagnosing fetal arrhythmia. However, few technologies currently are available to automatically calculate fetal cardiac time intervals (CTIs). OBJECTIVE: The purpose of this study was to develop a fetal heart rhythm intelligent quantification system (HR-IQS) for the automatic extraction of CTIs and establish the normal reference range for fetal CTIs. METHODS: A total of 6498 PWD spectrums of 2630 fetuses over the junction between the left ventricular inflow and outflow tracts were recorded across 14 centers. E, A, and V waves were manually labeled by 3 experienced fetal cardiologists, with 17 CTIs extracted. Five-fold cross-validation was performed for training and testing of the deep learning model. Agreement between the manual and HR-IQS-based values was evaluated using the intraclass correlation coefficient and Spearman's rank correlation coefficient. The Jarque-Bera test was applied to evaluate the normality of CTIs' distributions, and the normal reference range of 17 CTIs was established with quantile regression. Arrhythmia subset was compared with the non-arrhythmia subset using the Mann-Whitney U test. RESULTS: Significant positive correlation (P <.001) and moderate-to-excellent consistency (P <.001) between the manual and HR-IQS automated measurements of CTIs was found. The distribution of CTIs was non-normal (P <.001). The normal range (2.5th to 97.5th percentiles) was successfully established for the 17 CTIs. CONCLUSIONS: Using our HR-IQS is feasible for the automated calculation of CTIs in practice and thus could provide a promising tool for the assessment of fetal rhythm and function.

Outpatient Antibiotic Prescribing Patterns in Children among Primary Healthcare Institutions in China: A Nationwide Retrospective Study, 2017-2019.

There is scarce evidence to demonstrate the pattern of antibiotic use in children in China. We aimed to describe antibiotic prescribing practices among children in primary healthcare institutions (PHIs) in China. We described outpatient antibiotic prescriptions for children in PHIs from January 2017 to December 2019 at both the national and diagnostic levels, utilizing the antibiotic prescribing rate (APR), multi-antibiotic prescribing rate (MAPR), and broad-spectrum prescribing rate (BAPR). Generalized estimating equations were adopted to analyze the factors associated with antibiotic use. Among the total 155,262.2 weighted prescriptions for children, the APR, MAPR, and BAPR were 43.5%, 9.9%, and 84.8%. At the national level, J01DC second-generation cephalosporins were the most prescribed antibiotic category (21.0%, N = 15,313.0), followed by J01DD third-generation cephalosporins (17.4%, N = 12,695.8). Watch group antibiotics accounted for 55.0% of the total antibiotic prescriptions (N = 52,056.3). At the diagnostic level, respiratory tract infections accounted for 67.4% of antibiotic prescriptions, among which prescriptions with diagnoses classified as potentially bacterial RTIs occupied the highest APR (55.0%). For each diagnostic category, the MAPR and BAPR varied. Age, region, and diagnostic categories were associated with antibiotic use. Concerns were raised regarding the appropriateness of antibiotic use, especially for broad-spectrum antibiotics.

Segment anything model for medical images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.

Exploring Facilitators and Barriers to Delayed Antibiotic Prescribing in Rural Northwest China: A Qualitative Study Using the Theoretical Domains Framework and Behavior Change Wheel.

BACKGROUND: Antimicrobial resistance, exacerbated by antibiotic misuse, poses a global threat. Though delayed antibiotic prescribing (DAP) can mitigate antibiotic overuse, its adoption in developing nations, such as China, is limited. This study probed barriers and facilitators to DAP in Xinjiang, characterized by extensive rural landscapes and primary care institutions (PCIs). METHODS: Adopting a qualitative methodology, we conducted key informant interviews with thirty participants across six county hospitals in Xinjiang using VooV Meeting. Employing a two-stage sampling method targeting economically diverse areas, our interviews spanned physicians, pharmacists, patients, and caregivers. We organized the data according to the Theoretical Domains Framework (TDF) and the Behavior Change Wheel (BCW), spotlighting behavioral and policy elements impacting DAP. RESULTS: Our research included thirty interviewees. Twelve physicians contemplated delayed prescriptions, while five adult patients and six caregivers encountered recommendations for delayed antibiotic prescriptions. Six patients sought pharmacists' advice on antibiotic necessity. Prominent TDF domains were memory, attention, and beliefs about consequences. Critical intervention functions included education and environmental restructuring, while vital policy categories encompassed communication/marketing and guidelines. CONCLUSIONS: Countering antibiotic misuse and resistance in China necessitates overcoming barriers through strategic resource distribution, comprehensive education, rigorous training, and consistent monitoring, thereby promoting DAP adoption. The adoption of DAP in rural healthcare settings in China has the potential to significantly reduce antibiotic misuse, thereby mitigating the global threat of antimicrobial resistance.

Serological surveillance of GI norovirus reveals persistence of blockade antibody in a Jidong community-based prospective cohort, 2014-2018.

Introduction: Herd immunity against norovirus (NoV) is poorly understood in terms of its serological properties and vaccine designs. The precise neutralizing serological features of genotype I (GI) NoV have not been studied. Methods: To expand insights on vaccine design and herd immunity of NoVs, seroprevalence and seroincidence of NoV genotypes GI.2, GI.3, and GI.9 were determined using blockade antibodies based on a 5-year longitudinal serosurveillance among 449 residents in Jidong community. Results: Correlation between human histo-blood group antigens (HBGAs) and GI NoV, and dynamic and persistency of antibodies were also analyzed. Seroprevalence of GI.2, GI.3, and GI.9 NoV were 15.1%-18.0%, 35.0%-38.8%, and 17.6%-22.0%; seroincidences were 10.0, 21.0, and 11.0 per 100.0 person-year from 2014 to 2018, respectively. Blockade antibodies positive to GI.2 and GI.3 NoV were significantly associated with HBGA phenotypes, including blood types A, B (excluding GI.3), and O+; Lewis phenotypes Leb+/Ley+ and Lea+b+/Lex+y+; and secretors. The overall decay rate of anti-GI.2 antibody was -5.9%/year (95% CI: -7.1% to -4.8%/year), which was significantly faster than that of GI.3 [-3.6%/year (95% CI: -4.6% to -2.6%/year)] and GI.9 strains [-4.0%/year (95% CI: -4.7% to -3.3%/year)]. The duration of anti-GI.2, GI.3, and GI.9 NoV antibodies estimated by generalized linear model (GLM) was approximately 2.3, 4.2, and 4.8 years, respectively. Discussion: In conclusion, enhanced community surveillance of GI NoV is needed, and even one-shot vaccine may provide coast-efficient health benefits against GI NoV infection.