OBJECTIVE: To explore the prenatal and postnatal phenotypes of 22q11.2 microdeletion syndrome (22q11.2DS) and enhance clinical understanding of this condition. METHODS: Data were collected from 86 fetuses diagnosed with 22q11.2DS at four prenatal diagnostic centers across China between January 2014 and August 2025. Prenatal imaging findings, pregnancy outcomes, and postnatal conditions were analyzed. RESULTS: Among the 86 fetuses, complete ultrasound data were available for 65 cases. Cardiovascular abnormalities were observed in 42 cases, thymic hypoplasia or aplasia in 7 cases, urinary system anomalies in 6 cases, nuchal translucency (NT) thickening in 7 cases, butterfly vertebrae, clubfoot, omphalocele and diaphragmatic hernia in 1 case each, cleft lip and palate in 2 cases, and ultrasound soft markers in 13 cases. The parents of 9 fetuses opted to continue with the pregnancy. Among these, 6 showed no significant ultrasound abnormalities and no related phenotypes postnatally, while the remaining 3 exhibited ultrasound anomalies with postnatal manifestations including developmental delay, immunodeficiency, and cardiac defects. CONCLUSION Fetuses with 22q11.2DS may exhibit various ultrasound abnormalities in multiple systems before and after birth. In addition to cardiovascular anomalies, they may also present with thymic hypoplasia or aplasia, thickened NT, and urinary abnormalities. Fetuses with thickened NT or thymic anomalies should be closely monitored, and thymic assessment should be included in routine prenatal imaging evaluations. For fetuses with 22q11.2DS who show no ultrasound abnormalities, the risk of developing severe phenotypes after birth is relatively low, but occult palate clefts and psychiatric disorders cannot be ruled out. Due to limitations in sample size and follow-up duration, above conclusions require further validation through large-scale prospective studies.
Humans ; Female ; Pregnancy ; Ultrasonography, Prenatal ; DiGeorge Syndrome/genetics* ; Adult ; Male ; Follow-Up Studies ; Fetus/diagnostic imaging* ; Phenotype ; Infant, Newborn

OBJECTIVE: To explore the genotype-phenotype correlation in a Chinese family with structural brain abnormalities due to variant of the TUBB gene. METHODS: A family undergoing prenatal diagnosis at Beijing Obstetrics and Gynecology Hospital in October 2024 was selected as the study subject. Clinical data were collected. Amniotic fluid sample was subjected to chromosomal copy number variation sequencing (CNV-seq). Trio whole-exome sequencing (Trio-WES) was carried out on the amniotic fluid and parental blood samples, and candidate variant was verified by Sanger sequencing. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 2023-KY-076-01). RESULTS: Both prenatal ultrasound and fetal MRI showed deviation of brain midline, unilateral lateral ventriculomegaly, and bilateral gyral asymmetry. Trio-WES revealed that the fetus has harbored a maternally derived heterozygous missense variant of the TUBB gene [NM_178014.4: c.155A>G (p.N52S)]. Sanger sequencing confirmed that the woman and a previously terminated fetus both harbored the same variant. Both the proband and two fetuses exhibited similar neuroimaging abnormalities including midline deviation and asymmetrical gyri. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was classified as likely pathogenic (PM2_Supporting+PS2_Moderate+PS3). CONCLUSION The heterozygous c.155A>G (p.N52S) variant was the TUBB gene probably underlay the pathogenesis of the structural brain abnormalities in this family. Above findings have expanded the phenotypic spectrum associated with the variant and facilitated the prenatal diagnosis for this family.
Humans ; Female ; Pregnancy ; Prenatal Diagnosis ; Tubulin/genetics* ; Adult ; Brain/diagnostic imaging* ; Male ; Pedigree ; DNA Copy Number Variations/genetics* ; Exome Sequencing ; Genetic Association Studies ; Magnetic Resonance Imaging

OBJECTIVE: To investigate the genetic etiology of a fetus diagnosed with Mandibulofacial dysostosis with microcephaly (MFDM). METHODS: A fetus that underwent prenatal diagnosis at Beijing Obstetrics and Gynecology Hospital, Capital Medical University, on May 19, 2025 was selected for analysis. Results of fetal ultrasound findings, chromosomal karyotyping, copy number variation sequencing (CNV-seq), and whole-exome sequencing (WES) were collected. Sanger sequencing was performed for familial validation of the pathogenic variant. The Human Protein Atlas (HPA), STRING, and Simple ClinVar databases were queried to characterize the biological features of the candidate gene. Three-dimensional structures of the wild-type and variant proteins were modeled and analyzed, and the evolutionary conservation of the affected amino acid was assessed using UGENE. Prenatal phenotypes associated with EFTUD2 variants were summarized through a review of the literature. This study was approved by the Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Ethics No.: 2025-KY-029-01). RESULTS: At 23⁺² weeks of gestation, ultrasound examination revealed bilateral microtia with low-set ears, mild micrognathia with a reduced mandibular-facial angle, a single umbilical artery, a slightly narrow aortic diameter, and trivial mitral regurgitation. Amniotic fluid karyotyping and CNV-seq showed no abnormalities. WES identified a de novo, previously unreported EFTUD2 variant, c.698dupA (p.V235Gfs*27), in the fetus. This frameshift variant is predicted to alter the structural integrity of the EFTUD2 protein. Literature review indicated that micrognathia and microtia or low-set ears are the most common sonographic features in fetuses with EFTUD2 variants, while secondary findings may include abnormal stomach bubble, cleft palate, single umbilical artery, gastrointestinal atresia, polyhydramnios, and reduced aortic diameter. CONCLUSION The EFTUD2: c.698dupA (p.V235Gfs*27) variant is likely the genetic cause underlying MFDM in this fetus.
Humans ; Mandibulofacial Dysostosis/diagnostic imaging* ; Microcephaly/diagnostic imaging* ; Female ; Pregnancy ; Ribonucleoprotein, U5 Small Nuclear/chemistry* ; Peptide Elongation Factors/chemistry* ; Fetus ; DNA Copy Number Variations/genetics* ; Adult ; Ultrasonography, Prenatal

BACKGROUND AND OBJECTIVES

The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.

A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated.

With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices.

qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.

INTRODUCTION: Medical errors commonly occur in medical imaging departments. These errors are frequently influenced by patient safety culture. This study aimed to develop a suitable patient safety culture assessment tool for medical imaging departments. METHODS: Staff members of a teaching hospital medical imaging department were invited to complete the generic short version of the Safety Attitude Questionnaire (SAQ). Internal consistency and reliability were evaluated using Cronbach's α. Confirmatory factor analysis (CFA) was conducted to examine model fit. A cut-off of 60% was used to define the percentage positive responses (PPR). PPR values were compared between occupational groups. RESULTS: A total of 300 complete responses were received and the response rate was 75.4%. In reliability analysis, the Cronbach's α for the original 32-item SAQ was 0.941. Six subscales did not demonstrate good fit with CFA. A modified five-subscale, 22-item model (SAQ-MI) showed better fit (goodness-to-fit index ≥0.9, comparative fit index ≥ 0.9, Tucker-Lewis index ≥0.9 and root mean square error of approximation ≤0.08). The Cronbach's α for the 22 items was 0.921. The final five subscales were safety and teamwork climate, job satisfaction, stress recognition, perception of management and working condition, with PPR of 62%, 68%, 57%, 61% and 60%, respectively. Statistically significant differences in PPR were observed between radiographers, doctors and others occupational groups. CONCLUSION The modified five-factor, 22-item SAQ-MI is a suitable tool for the evaluation of patient safety culture in a medical imaging department. Differences in patient safety culture exist between occupation groups, which will inform future intervention studies.
Humans ; Surveys and Questionnaires ; Patient Safety ; Attitude of Health Personnel ; Diagnostic Imaging ; Reproducibility of Results ; Male ; Female ; Adult ; Job Satisfaction ; Factor Analysis, Statistical ; Middle Aged ; Hospitals, Teaching ; Safety Management ; Organizational Culture ; Medical Errors/prevention & control*

INTRODUCTION: Foreign body (FB) ingestion is a common paediatric emergency. While guidelines exist for urgent intervention, less is known of the natural progress of FBs passing through the gastrointestinal tract (GIT). We reviewed these FB transit times in an outpatient cohort. METHODS: A retrospective review was performed on all children (≤18 years) treated for radiopaque FB ingestion at two major tertiary paediatric centres from 2015 to 2016. Demographic data, FB types, outcomes and hospital visits (emergency department [ED] and outpatient) were recorded. All cases discharged from the ED with outpatient follow-up were included. We excluded those who were not given follow-up appointments and those admitted to inpatient wards. We categorised the outcomes into confirmed passage (ascertained via abdominal X-ray or reported direct stool visualisation by patients/caregivers) and assumed passage (if patients did not attend follow-up appointments). RESULTS: Of the 2,122 ED visits for FB ingestion, 350 patients who were given outpatient follow-up appointments were reviewed (median age 4.35 years [range: 0.5-14.7], 196 [56%] male). The largest proportion (16%) was aged 1-2 years. Coins were the most common ingested FB, followed by toys. High-risk FB (magnets or batteries) formed 9% of cases ( n =33). The 50 th centile for FB retention was 8, 4 and 7 days for coins, batteries and other radiopaque FBs, respectively; all confirmed passages occurred at 37, 7 and 23 days, respectively. Overall, 197 (68%) patients defaulted on their last given follow-up. CONCLUSION This study provides insight into the transit times of FB ingested by children, which helps medical professionals to decide on the optimal time for follow-up visits and provide appropriate counsel to caregivers.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Male ; Eating ; Emergency Service, Hospital ; Foreign Bodies/diagnostic imaging* ; Gastrointestinal Tract/diagnostic imaging* ; Gastrointestinal Transit ; Retrospective Studies ; Singapore ; Tertiary Care Centers

INTRODUCTION: This study was conducted to evaluate the efficacy of postoperative computed tomography (CT) measurements of aortic lumen volumes in predicting aortic-related complications following acute type A aortic dissection (ATAAD) repair. METHODS: We conducted a single-institution retrospective aortic volumetric analysis of patients after ascending aorta replacement performed during 2001-2015. The volumetric measurements of total lumen (total-L), true lumen (TL), false lumen (FL), as well as the TL:FL ratio from the first and second postoperative computer angiograms were obtained. A generalised structural equation model was created to analyse the predictive utility of TL:FL ratio. RESULTS: One hundred and twenty-five patients underwent surgical intervention, of whom 97 patients were eventually discharged and analysed for postoperative complications. A total of 19 patients were included in the final analysis. Patients with late postoperative aortic complications had a significantly higher FL volume and total-L volume on the first (FL volume P = 0.041, total-L volume P = 0.05) and second (FL volume P = 0.01, total-L volume P = 0.007) postoperative scans. The odds of having aortic complications were raised by 1% with a 1 cm 3 increase in total-L volume and by 2% with a 1 cm 3 increase in FL volume. The TL:FL ratio was significantly lower in patients who developed complications. CONCLUSION Postoperative CT volumetric measurements in patients who developed complications are characterised by a significant increase in the FL volume and total-L volume from the first postoperative scans. Patients with disproportionately expanded FL presenting with TL:FL ratios less than 1 were associated with aortic complications. Hence, the TL:FL ratio may be a reliable and useful parameter to monitor postoperative disease progression and to evaluate the risk of late complications in ATAAD patients.
Humans ; Male ; Female ; Retrospective Studies ; Singapore ; Aortic Dissection/diagnostic imaging* ; Middle Aged ; Postoperative Complications/diagnostic imaging* ; Aged ; Tomography, X-Ray Computed ; Aortic Aneurysm/diagnostic imaging* ; Aorta/surgery* ; Adult ; Treatment Outcome ; Computed Tomography Angiography

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

This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8), the system further deduces key characteristics of four acupoints: Shangwan (CV13), Qugu (CV2), and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space, achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education, diagnostic assistance, and advanced TCM equipment, such as intelligent acupuncture robots, facilitating the standardization and intelligent advancement of acupuncture.
Acupuncture Points ; Humans ; Deep Learning ; Abdomen/diagnostic imaging* ; Neural Networks, Computer ; Acupuncture Therapy ; Image Processing, Computer-Assisted