Development of a dental diet-tracking mobile app for improved caries-related dietary behaviours: Key features and pilot evaluation of quality

Objective

Diet significantly contributes to dental decay (caries) yet monitoring and modifying patients' diets is a challenge for many dental practitioners. While many oral health and diet-tracking mHealth apps are available, few focus on the dietary risk factors for caries. This study aims to present the development and key features of a dental-specific mobile app for diet monitoring and dietary behaviour change to prevent caries, and pilot data from initial user evaluation.

Methods

A mobile app incorporating a novel photo recognition algorithm and a localised database of 208,718 images for food item identification was developed. The design and development process were iterative and incorporated several behaviour change techniques commonly used in mHealth. Pilot evaluation of app quality was assessed using the end-user version of the Mobile Application Rating Scale (uMARS).

Results

User feedback from the beta-testing of the prototype app spurred the improvement of the photo recognition algorithm and addition of more user-centric features. Other key features of the final app include real-time prompts to drive actionable behaviour change, goal setting, comprehensive oral health education modules, and visual metrics for caries-related dietary factors (sugar intake, meal frequency, etc.). The final app scored an overall mean (standard deviation) of 3.6 (0.5) out of 5 on the uMARS scale.

Conclusion

We developed a novel diet-tracking mobile app tailored for oral health, addressing a gap in the mHealth landscape. Pilot user evaluations indicated good app quality, suggesting its potential as a useful clinical tool for dentists and empowering patients for self-monitoring and behavioural management.

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review

Background

There is emerging evidence which suggests the utility of artificial intelligence (AI) in the diagnostic assessment and pre-treatment evaluation of thyroid eye disease (TED). This scoping review aims to (1) identify the extent of the available evidence (2) provide an in-depth analysis of AI research methodology of the studies included in the review (3) Identify knowledge gaps pertaining to research in this area.

Methods

This review was performed according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA). We quantify the diagnostic accuracy of AI models in the field of TED assessment and appraise the quality of these studies using the modified QUADAS-2 tool.

Results

A total of 13 studies were included in this review. The most common AI models used in these studies are convolutional neural networks (CNN). The majority of the studies compared algorithm performance against healthcare professionals. The overall risk of bias and applicability using the modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool led to most of the studies being classified as low risk, although higher deficiency was noted in the risk of bias in flow and timing.

Conclusions

While the results of the review showed high diagnostic accuracy of the AI models in identifying features of TED relevant to disease assessment, deficiencies in study design causing study bias and compromising study applicability were noted. Moving forward, limitations and challenges inherent to machine learning should be addressed with improved standardized guidance around study design, reporting, and legislative framework.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT

PURPOSE

To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.

METHODS

We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC.

RESULTS

Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001).

CONCLUSION

A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT

Introduction

Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.

Methods

Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated.

Results

Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001).

Conclusion

Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

Background

Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.

Purpose

To develop a DL model for automated classification of MESCC on MRI.

Materials and Methods

Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.

Results

Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.

Conclusion

A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI

Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consistency of reporting. Purpose To develop a DL model for automated detection and classification of lumbar central canal, lateral recess, and neural -foraminal stenosis. Materials and Methods In this retrospective study, lumbar spine MRI scans obtained from September 2015 to September 2018 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality, as well as postgadolinium studies and studies of patients with scoliosis, were excluded. Axial T2-weighted and sagittal T1-weighted images were used. Studies were split into an internal training set (80%), validation set (9%), and test set (11%). Training data were labeled by four radiologists using predefined gradings (normal, mild, moderate, and severe). A two-component DL model was developed. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second CNN for classification. An internal test set was labeled by a musculoskeletal radiologist with 31 years of experience (reference standard) and two subspecialist radiologists (radiologist 1: A.M., 5 years of experience; radiologist 2: J.T.P.D.H., 9 years of experience). DL model performance on an external test set was evaluated. Detection recall (in percentage), interrater agreement (Gwet κ), sensitivity, and specificity were calculated. Results Overall, 446 MRI lumbar spine studies were analyzed (446 patients; mean age ± standard deviation, 52 years ± 19; 240 women), with 396 patients in the training (80%) and validation (9%) sets and 50 (11%) in the internal test set. For internal testing, DL model and radiologist central canal recall were greater than 99%, with reduced neural foramina recall for the DL model (84.5%) and radiologist 1 (83.9%) compared with radiologist 2 (97.1%) (P < .001). For internal testing, dichotomous classification (normal or mild vs moderate or severe) showed almost-perfect agreement for both radiologists and the DL model, with respective κ values of 0.98, 0.98, and 0.96 for the central canal; 0.92, 0.95, and 0.92 for lateral recesses; and 0.94, 0.95, and 0.89 for neural foramina (P < .001). External testing with 100 MRI scans of lumbar spines showed almost perfect agreement for the DL model for dichotomous classification of all ROIs (κ, 0.95-0.96; P < .001). Conclusion A deep learning model showed comparable agreement with subspecialist radiologists for detection and classification of central canal and lateral recess stenosis, with slightly lower agreement for neural foraminal stenosis at lumbar spine MRI. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute

We consider privacy-preserving computation of big data using trusted computing primitives with limited private memory. Simply ensuring that the data remains encrypted outside the trusted computing environment is insufficient to preserve data privacy, for data movement observed during computation could leak information. While it is possible to thwart such leakage using generic solution such as ORAM [42], designing efficient privacy-preserving algorithms is challenging. Besides computation efficiency, it is critical to keep trusted code bases lean, for large ones are unwieldy to vet and verify. In this paper, we advocate a simple approach wherein many basic algorithms (e.g., sorting) can be made privacy-preserving by adding a step that securely scrambles the data before feeding it to the original algorithms. We call this approach Scramble-then-Compute (StC), and give a sufficient condition whereby existing external memory algorithms can be made privacy-preserving via StC. This approach facilitates code-reuse, and its simplicity contributes to a smaller trusted code base. It is also general, allowing algorithm designers to leverage an extensive body of known efficient algorithms for better performance. Our experiments show that StC could offer up to 4.1× speedups over known, application-specific alternatives.

Contextual crowd intelligence

Most data analytics applications are industry/domain specific, e.g., predicting patients at high risk of being admitted to intensive care unit in the healthcare sector or predicting malicious SMSs in the telecommunication sector. Existing solutions are based on "best practices", i.e., the systems' decisions are knowledge-driven and/or data-driven . However, there are rules and exceptional cases that can only be precisely formulated and identified by subject-matter experts (SMEs) who have accumulated many years of experience. This paper envisions a more intelligent database management system (DBMS) that captures such knowledge to effectively address the industry/domain specific applications. At the core, the system is a hybrid human-machine database engine where the machine interacts with the SMEs as part of a feedback loop to gather, infer, ascertain and enhance the database knowledge and processing. We discuss the challenges towards building such a system through examples in healthcare predictive analysis -- a popular area for big data analytics.

Outcome of liver transplantation for children with liver disease

INTRODUCTION

The advent of liver transplantation has revolutionised the outcome of children with both acute liver failure and chronic end-stage liver disease. The aim of this study was to review the outcome of all paediatric liver transplants performed since the National Liver Transplant Programme began in 1990.

METHODS

A retrospective review of all paediatric liver transplants from 1990 to December 2004 was performed.

RESULTS

46 liver transplants were performed in 43 children, of whom 23 (53.3 percent) were female. Median age at transplant was 21 months (range 11 months to 14 years). The most common indication for liver transplant was biliary atresia (71.7 percent). Living-related transplants accounted for 63 percent (29). Re-transplant rate was 6.5 percent with allograft loss as a result of hepatic artery thrombosis (two) and hepatic vein thrombosis (one). Tacrolimus was the primary immunosuppressive agent used in 89 percent of patients, with a 19.6 percent incidence of acute allograft rejection within the first six months. There were nine deaths. They were related to portal vein thrombosis (three), chronic rejection (one), sepsis (two), post-transplant lymphoproliferative disease (two) and primary graft non-function (one). Overall actuarial one- and five-year survival rate was 85.7 percent and 81.8 percent, respectively.

CONCLUSION

Liver transplantation is an established form of intervention for end-stage liver disease and a variety of liver-related metabolic disease. Our results are comparable to those of well-established liver transplant centres.

Tailored reversible watermarking schemes for authentication of electronic clinical atlas

It is accepted that digital watermarking is quite relevant in medical imaging. However, due to the special nature of clinical practice, it is often required that watermarking not introduce irreversible distortions to medical images. The electronic clinical atlas has such a need of "lossless" watermarking. We present two tailored reversible watermarking schemes for the clinical atlas by exploiting its inherent characteristics. We have implemented the schemes and our experimental results look very promising.

BLAST++: BLASTing queries in batches

BLAST++ is a tool that is integrated with NCBI BLAST, allowing multiple, say K, queries to be searched against a database concurrently. The results obtained by BLAST++ are identical to that obtained by executing BLAST on each of the K queries, but BLAST++ completes the processing in a much shorter time.

AVAILABILITY

http://xena1.ddns.comp.nus.edu.sg/~genesis/blast++

SUPPLEMENTARY INFORMATION

http://xena1.ddns.comp.nus.edu.sg/~genesis/blast++

Making the diagnosis of biliary atresia using the triangular cord sign and gallbladder length

BACKGROUND

To evaluate the accuracy and utility of the triangular cord sign and gallbladder length in diagnosing biliary atresia by sonography.

MATERIALS AND METHODS

Sixty fasted infants with cholestatic jaundice aged 2-12 weeks were examined sonographically using a 5-10 MHz linear array transducer, focusing on the triangular cord sign (as described by Choi et al. [1]), the gallbladder, and ducts. The triangular cord is defined as a triangular or tubular echogenic density seen immediately cranial to the portal vein bifurcation; it represents the fibrotic remnant of the obliterated cord in biliary atresia. The findings were blinded to blood chemistry, (99 m)Tc-DISIDA hepatobiliary scintigraphy, and liver biopsy. Diagnosis of biliary atresia was confirmed at surgery and histology. Non-biliary atresia infants resolved medically. Comparative charges of the various investigations was made.

RESULTS

++ Twelve infants had biliary atresia, and ten demonstrated a definite triangular cord. The two false-negatives had small or nonvisualized gallbladders. No false-positives were recorded. Gallbladder length ranged from 0-1.45 cm with a mean of 0. 52 cm in biliary atresia compared to a mean of 2.39 cm in nonbiliary atresia infants. (99 m)Tc-DISIDA hepatobiliary scintigraphy showed no excretion (false-positive) in 23 % of nonbiliary atresia cases. Scintigraphy and liver biopsy charges were 2 and 6 times that of sonography, respectively.

CONCLUSION

The triangular cord sign and gallbladder length together are noninvasive, inexpensive, and very useful markers for biliary atresia.

Association of ambient air-pollution levels with acute asthma exacerbation among children in Singapore

BACKGROUND

Air-pollution levels have been shown to be associated with increased morbidity of respiratory diseases.

METHODS

Data for ambient air-pollutant levels, meteorologic factors, and hospitalization or emergency room (ER) visits for acute asthma in Singapore children over a 5-year period (1990-4) were obtained and analyzed for associations by time-series methods.

RESULTS

Throughout this period, the annual mean and 24-h mean levels for sulfur dioxide (SO2), nitrogen dioxide (NO2), and total suspended particles (TSP) and maximum 1-h daily average for ozone were generally within the air-quality guidelines established by the World Health Organization (WHO). However, positive correlation between levels of each of these pollutants and daily ER visits for asthma was observed in children aged 3-12 years, but not among adolescents and young adults (13-21 years old). The association with SO2 and TSP persisted after standardization for meteorologic and temporal variables. An adjusted increase in 2.9 ER visits for every 20 microg/m3 increase in atmospheric SO2 levels, lagged by 1 day, was observed on days when levels were above 68 microg/m3. With TSP, an adjusted increase of 5.80 ER visits for every 20 microg/m3 increase in its daily atmospheric levels, lagged by 1 day, was observed on days with levels above 73 microg/m3. Similar results were also obtained after controlling for autocorrelation by time-series analysis.

CONCLUSIONS

These associations were observed even though the overall levels of all pollutants were generally within the air-quality guidelines established by the WHO. These findings suggest that asthmatic children are susceptible to increased levels of air pollutants, particularly SO2 and TSP, although the ambient levels are generally within "acceptable" ranges.

The incidence of IDDM in Singapore children

OBJECTIVE

To determine the incidence of insulin dependent diabetes mellitus (IDDM) in children 0-12 years of age in Singapore, which has a population of 2.9 million.

METHODS

The primary source was a 2-year phone and mail survey of doctors in the government and government restructured hospitals and the private sector. The secondary source was the membership records of the Diabetes Society of Singapore.

RESULTS

Using the capture-recapture method, ascertainment was assessed to be 92.2% complete. The age standardised incidence rate was 2.46 per 100,000 children 0-12 years old, for the period 1992-1994 (95% confidence interval: 2.16-2.75). The data seemed to indicate a rising incidence of IDDM in this population, being 1.4/100,000 in 1992, 2.4/100,000 in 1993 and 3.8/100,000 in 1994. The male: female ratio is 1:1.85. There was seasonal variation with fewer cases from July to October and more from November to May. Five percent of patients had a first degree relative with IDDM. Malays appeared to have a lower incidence (1.23/100,000) compared to the Chinese (2.25/100,000) and the Indians (5.78/100,000).

CONCLUSIONS

The incidence of IDDM in Singapore children is similar to that reported for Hong Kong and Japan, but higher than that for Shanghai. The female preponderance is similar to that seen in other Asian population. The data suggests a rising incidence of IDDM in Singapore and differences in incidence between the Malays, Chinese and Indians, but further observations are needed.

Normalization of vitamin B12 absorption after ileal resection in children

Impaired Vitamin B12 absorption after significant ileal resection has been reported to be permanent, although partial recovery after ileal bypass can occur. Three children are presented in whom Vitamin B12 malabsorption returned to normal 6-8 years after ileal resection. This was due probably to adaptation of the remaining small bowel, although spontaneous resolution of bacterial overgrowth is a possible explanation. An abnormal Schilling test after ileal resection does not automatically imply the need for life-long Vitamin B12 injections.

Acute appendicitis in Singapore children--some clinical aspects

This retrospective study of 132 patients less than 12 years of age with Appendectomy done for Acute Appendicitis showed histological confirmation in 106 patients (80.3%) and a "negative appendix" rate of 19.7%. The appendix was perforated in 31 patients (23.5%). In those patients with confirmed Acute Appendicitis, males predominate (1.7 males: 1 female) and the peak incidence was in those 9 years of age or more. Abdominal pain was present in all patients except a 13 month old infant. Abdominal tenderness was also elicited in all patients except one. Fever was present in 83 patients (78.3%), vomiting in 82 patients (77.4%) and diarrhoea in 19 patients (17.9%). There were 2 deaths in this review, giving a mortality rate of 1.9%. Postoperative complications include wound infection (13.2%), pelvic abscess (0.9%), ileus (0.9%) and adhesion obstruction (0.9%).

Acute gastroenteritis in children--aetiology and comparison of two modes of treatment

287 patients with Acute Gastroenteritis were studied. Rotavirus accounted for 29.7% and bacterial aetiology for 9.3% of the cases. In the early infant age group (0-6 months), bacterial aetiology (28.8%) was commoner than Rotavirus aetiology (7.7%). In the older age groups, Rotavirus was more common than bacteria as a cause for Gastroenteritis.

Delayed recovery following acute gastroenteritis in children

The purpose of this study is to identify the factors which are responsible for the delayed recovery in acute gastroenteritis (GE) in children. 372 children admitted to the University Department of Paediatrics were reviewed. 29 children (7.8%) required prolonged hospitalisation because of diarrhoea (delayed recovery group) and 343 children (92.2%) had short hospitalisation (normal recovery group). There was no significant difference in terms of race, sex, duration of symptoms before admission and severity of dehydration between the two groups. However, those with delayed recovery were significantly younger (mean age 14.4 months) than the normal recovery group (mean age 23.3 months). In the delayed recovery group, 32% were due to bacterial diarrhoea compared to 5.0% in the normal recovery group (p less than 0.05). The body weight of 31% of the delayed recovery group was less than the 3rd percentile (p less than 0.05). It is concluded that children take a longer time to recover from the bacterial GE than from non-bacterial GE. The younger children will take a longer time to recover from an episode of diarrhoea. The body weight also plays an important role in recovery, reflecting the importance of nutrition in the process of recovery.