ACU2E-Net: A novel predict-refine attention network for segmentation of soft-tissue structures in ultrasound images.

Segmentation of anatomical structures in ultrasound images is a challenging task due to existence of artifacts inherit to the modality such as speckle noise, attenuation, shadowing, uneven textures and blurred boundaries. This paper presents a novel attention-based predict-refine network, called ACU2E-Net, for segmentation of soft-tissue structures in ultrasound images. The network consists of two modules: a predict module, which is built upon our newly proposed attentive coordinate convolution; and a novel multi-head residual refinement module, which consists of three parallel residual refinement modules. The attentive coordinate convolution is designed to improve the segmentation accuracy by perceiving the shape and positional information of the target anatomy. The proposed multi-head residual refinement module reduces both segmentation biases and variances by integrating residual refinement and ensemble strategies. Moreover, it avoids multi-pass training and inference commonly seen in ensemble methods. To show the effectiveness of our method, we collect a comprehensive dataset of thyroid ultrasound scans from 12 different imaging centers, and evaluate our proposed network against state-of-the-art segmentation methods. Comparisons against state-of-the-art models demonstrate the competitive performance of our newly designed network on both the transverse and sagittal thyroid images. Ablation studies show that proposed modules improve the segmentation Dice score of the baseline model from 79.62% to 80.97% and 82.92% while reducing the variance from 6.12% to 4.67% and 3.21% in transverse and sagittal views, respectively.

Interobserver Variability of Hip Dysplasia Indices on Sweep Ultrasound for Novices, Experts, and Artificial Intelligence.

BACKGROUND: Ultrasound for developmental dysplasia of the hip (DDH) is challenging for nonexperts to perform and interpret. Recording "sweep" images allows more complete hip assessment, suitable for automation by artificial intelligence (AI), but reliability has not been established. We assessed agreement between readers of varying experience and a commercial AI algorithm, in DDH detection from infant hip ultrasound sweeps. METHODS: We selected a full spectrum of poor-to-excellent quality images and normal to severe dysplasia, in 240 hips (120 single 2-dimensional images, 120 sweeps). For 12 readers (radiologists, sonographers, clinicians and researchers; 3 were DDH subspecialists), and a ultrasound-FDA-cleared AI software package (Medo Hip), we calculated interobserver reliability for alpha angle measurements by intraclass correlation coefficient (ICC2,1) and for DDH classification by Randolph Kappa. RESULTS: Alpha angle reliability was high for AI versus subspecialists (ICC=0.87 for sweeps, 0.90 for single images). For DDH diagnosis from sweeps, agreement was high between subspecialists (kappa=0.72), and moderate for nonsubspecialists (0.54) and AI (0.47). Agreement was higher for single images (kappa=0.80, 0.66, 0.49). AI reliability deteriorated more than human readers for the poorest-quality images. The agreement of radiologists and clinicians with the accepted standard, while still high, was significantly poorer for sweeps than 2D images (P<0.05). CONCLUSIONS: In a challenging exercise representing the wide spectrum of image quality and reader experience seen in real-world hip ultrasound, agreement on DDH diagnosis from easily obtained sweeps was only slightly lower than from single images, likely because of the additional step of selecting the best image. AI performed similarly to a nonsubspecialist human reader but was more affected by low-quality images.

Integrating User-Input into Deep Convolutional Neural Networks for Thyroid Nodule Segmentation.

Delineation of thyroid nodule boundaries is necessary for cancer risk assessment and accurate categorization of nodules. Clinicians often use manual or bounding-box approach for nodule assessment which leads to subjective results. Consequently, agreement in thyroid nodule categorization is poor even among experts. Computer-aided diagnosis systems could reduce this variability by minimizing the extent of user interaction and by providing precise nodule segmentations. In this study, we present a novel approach for effective thyroid nodule segmentation and tracking using a single user click on the region of interest. When a user clicks on an ultrasound sweep, our proposed model can predict nodule segmentation over the entire sequence of frames. Quantitative evaluations show that the proposed method out-performs the bounding box approach in terms of the dice score on a large dataset of 372 ultrasound images. The proposed approach saves expert time and reduces the potential variability in thyroid nodule assessment. The proposed one-click approach can save clinicians time required for annotating thyroid nodules within ultrasound images/sweeps. With minimal user interaction we would be able to identify the nodule boundary which can further be used for volumetric measurement and characterization of the nodule. This approach can also be extended for fast labeling of large thyroid imaging datasets suitable for training machine-learning based algorithms.

Thyroid Nodule Segmentation and Classification Using Deep Convolutional Neural Network and Rule-based Classifiers.

Thyroid cancer has a high prevalence all over the world. Accurate thyroid nodule diagnosis can lead to effective treatment and decrease the mortality rate. Ultrasound imaging is a safe, portable, and inexpensive tool for thyroid nodule monitoring. However, the widespread use of ultrasound has also resulted in over-diagnosis and over-treatment of nodules. There is also large variability in the assessment and characterization of nodules. Thyroid nodule classification requires precise delineation of the nodule boundary which is tedious and time- consuming. Automatic segmentation of nodule boundaries is highly desirable, however, it is challenging due to the wide range of nodule appearances, shapes, and sizes. In this study, we propose an end-to-end pipeline for nodule segmentation and classification. A residual dilated UNet (resDUnet) model is proposed for nodule segmentation. The output of resDUnet is fed to two rule-based classifiers to categorize the composition and echogenicity of the segmented nodule. We evaluate our segmentation method on a large dataset of 352 ultrasound images reviewed by a certified radiologist. When compared with ground-truth, resDUnet gives a higher Dice score than the standard UNet (82% vs. 81%). Our method requires minimal user interaction and it is robust to reasonable variations in the user-specified region-of-interest. We expect the proposed method to reduce variability in thyroid nodule assessment which results in more efficient and cost-effective monitoring of thyroid cancer.

Radiation exposure during computerized tomography-based neuroimaging for acute ischemic stroke: a case-control study.

Patients and clinicians often raise concerns about radiation exposure to various organs during computerized tomography-based imaging. We evaluated radiation exposure during standard and low-dose imaging protocols for non-contrast computerized tomography, computerized tomography angiography and computerized tomography perfusion of the head. Whether reducing the radiation dose affected the image quality was also evaluated. Radiation data were retrieved for computerized tomography-based imaging studies performed for acute ischemic stroke patients during 2015. The volume-weighted computerized tomography dose index, dose-length product, scan length, effective dose and whole-body integral dose for brain, skin, eye, thyroid and red bone marrow were extracted from dose-tracking software. Dose metrics for low-dose protocols data were compared with standard protocols. The calculated effective doses for non-contrast computerized tomography, computerized tomography angiography and computerized tomography perfusion were 2.56 ± 0.67 mSv, 4.45 ± 2.5 mSv, and 4.47 ± 0.85 mSv, respectively for 391 acute ischemic stroke patients. Corresponding radiation exposures for low-dose protocol (n = 31) were non-contrast computerized tomography (2.36 ± 0.65 mSv), computerized tomography angiography (1.57 ± 0.74 mSv) and computerized tomography perfusion (2.20 ± 0.55 mSv). Overall, the effective dose for one complete stroke imaging protocol (non-contrast computerized tomography + computerized tomography angiography + computerized tomography perfusion) for the standard-dose protocol was 11.48 mSv, which was reduced to 6.13 mSv (46.6% reduction) using a low-dose protocol (p < 0.001). Reduced radiation exposure was noted for other radiosensitive organs. Radiation exposures of sensitive organs are within acceptable limits with standard neuroimaging protocols for acute ischemic stroke. Lower-dose computerized tomography imaging protocols reduced the radiation doses without appreciable deterioration in image quality.

Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19.

There is a crucial need for quick testing and diagnosis of patients during the COVID-19 pandemic. Lung ultrasound is an imaging modality that is cost-effective, widely accessible, and can be used to diagnose acute respiratory distress syndrome in patients with COVID-19. It can be used to find important characteristics in the images, including A-lines, B-lines, consolidation, and pleural effusion, which all inform the clinician in monitoring and diagnosing the disease. With the use of portable ultrasound transducers, lung ultrasound images can be easily acquired, however, the images are often of poor quality. They often require an expert clinician interpretation, which may be time-consuming and is highly subjective. We propose a method for fast and reliable interpretation of lung ultrasound images by use of deep learning, based on the Kinetics-I3D network. Our learned model can classify an entire lung ultrasound scan obtained at point-of-care, without requiring the use of preprocessing or a frame-by-frame analysis. We compare our video classifier against ground truth classification annotations provided by a set of expert radiologists and clinicians, which include A-lines, B-lines, consolidation, and pleural effusion. Our classification method achieves an accuracy of 90% and an average precision score of 95% with the use of 5-fold cross-validation. The results indicate the potential use of automated analysis of portable lung ultrasound images to assist clinicians in screening and diagnosing patients.

Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks.

Thyroid cancer is the most common endocrine cancer and its incidence has continuously increased worldwide. In this paper, we focus on the challenging problem of nodule detection from ultrasound scans. In current clinical practice, this task is performed manually, which is tedious, subjective and highly depends on the clinical experience of radiologists. We propose a novel deep neural network architecture with carefully designed loss function regularization, and network hyperparameters to perform nodule detection without complex post-processing refinement steps. The local training and validation datasets consist of 2461 and 820 ultrasound frames acquired from 60 and 20 patients with a high degree of variability, respectively. The core of the proposed method is a deep learning framework based on multi-task model Mask R-CNN. We have developed a loss function with regularization that prioritizes detection over segmentation. Validation was conducted for 821 ultrasound frames from 20 patients. The proposed model can detect various types of thyroid nodules. The experimental results indicate that our proposed method is effective in thyroid nodule detection. Comparisons with the results by Faster R-CNN and conventional Mask R-CNN demonstrate that the proposed model outperforms the prior state-of-the-art detection methods.

Imaging findings on contrast-enhanced ultrasound, magnetic resonance imaging and hybrid positron emission tomography in Takayasu arteritis: a case report.

This case report demonstrates the potential of contrast-enhanced ultrasound (CEUS) in diagnosing active arterial wall inflammation in a symptomatic patient with Takayasu arteritis (TA). To our knowledge, this is the first case which demonstrates pictorial correlation of arterial wall neovascularity on CEUS with mural edema on magnetic resonance imaging and metabolic activity on positron emission tomography - computed tomography in the same patient. As TA is a chronic disease which requires long-term follow-up, CEUS could be the potential imaging modality of choice as it is radiation-free, non-nephrotoxic and easily available.

Utilization of contrast-enhanced ultrasound in the evaluation of craniofacial osseous lesions: A case report.

A 14-year-old boy undergoing brain MRI had an incidental avidly enhancing lobulated lesion in the left superolateral orbital rim with associated cortical erosion. Apart from Contrast-enhanced Magnetic Resonance Imaging (MRI), and Computed Tomography (CT), Contrast-Enhanced Ultrasound (CEUS) was obtained prior to a biopsy. It provided additional information about the microvasculature and an orbital biopsy was subsequently performed through an upper eyelid crease incision with minimal blood loss and no postoperative complications. Histopathological examination revealed features which were compatible with the diagnosis of LCH. The authors propose that CEUS may be considered as an adjunct and possibly alternative imaging modality for the evaluation of craniofacial osseous lesions, especially in the orbital region (due to the known radio-sensitivity of the eyes) and in pediatric patients, to minimize the risk of ionizing-radiation exposure.

Ultrasound Guided Optoacoustic Tomography in Assessment of Tumor Margins for Lumpectomies.

PURPOSE: To determine the accuracy of a handheld ultrasound-guided optoacoustic tomography (US-OT) probe developed for human deep-tissue imaging in ex vivo assessment of tumor margins postlumpectomy. METHODS: A custom-built two-dimensional (2D) US-OT-handheld probe was used to scan 15 lumpectomy breast specimens. Optoacoustic signals acquired at multiple wavelengths between 700 and 1100 nm were reconstructed using model linear algorithm, followed by spectral unmixing for lipid and deoxyhemoglobin (Hb). Distribution maps of lipid and Hb on the anterior, posterior, superior, inferior, medial, and lateral margins of the specimens were inspected for margin involvement, and results were correlated with histopathologic findings. The agreement in tumor margin assessment between US-OT and histopathology was determined using the Bland-Altman plot. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of margin assessment using US-OT were calculated. RESULTS: Ninety margins (6 × 15 specimens) were assessed. The US-OT probe resolved blood vessels and lipid up to a depth of 6 mm. Negative and positive margins were discriminated by marked differences in the distribution patterns of lipid and Hb. US-OT assessments were concordant with histopathologic findings in 87 of 89 margins assessed (one margin was uninterpretable and excluded), with diagnostic accuracy of 97.9% (kappa = 0.79). The sensitivity, specificity, PPV, and NPV were 100% (4/4), 97.6% (83/85), 66.7% (4/6), and 100% (83/83), respectively. CONCLUSION: US-OT was capable of providing distribution maps of lipid and Hb in lumpectomy specimens that predicted tumor margins with high sensitivity and specificity, making it a potential tool for intraoperative tumor margin assessment.

Effect of a Soft Robotic Sock Device on Lower Extremity Rehabilitation Following Stroke: A Preliminary Clinical Study With Focus on Deep Vein Thrombosis Prevention.

BACKGROUND AND OBJECTIVE: Immobility of the lower extremity due to medical conditions such as stroke can lead to medical complications such as deep vein thrombosis or ankle contracture, and thereafter prolonged recovery process of the patients. In this preliminary clinical study, we aimed to examine the effect of a novel soft robotic sock device, capable of providing assisted ankle exercise, in improving blood flow in the lower limb to prevent the complication of strokes such as deep vein thrombosis and joint contracture. METHODS: Stroke patients were recruited (n = 17) to compare patients using the conventional pneumatic compression device with our robotic sock device on separate days. The primary outcome was to compare the venous flow profile of the superficial femoral vein in terms of the time average mean velocity and volumetric flow. The secondary outcome was to identify the ankle joint range of motion with the assistance of the device. RESULTS: We noted improvements in the venous profile at the early phase of the device use, though its efficacy seemed to drop with time, as compared to the IPC device, where there was a significant improvement in the venous profile. The ankle joint dorsiflexion-plantarflexion range of motion assisted by the device was 11.5±6.3°. Conclusion and clinical impact: The current version of our sock device appears to be capable of improving venous blood flow in the early phase of device use and assisting with ankle joint exercise. The insights from this preliminary clinical study will serve as the basis for further improvement of the device and subsequent conduct of a longitudinal clinical trial. FUNDING: National Health Innovation Centre Singapore (NHIC) grant, R-172-000-391-511, MOE AcRF Tier 1 R-397-000-301-114.

Pushing the limits of immune-related response: a case of "extreme pseudoprogression".

The advent of immune checkpoint targeted immunotherapy has seen a spectrum of immune-related phenomena in both tumor responses and toxicities. We describe a case of pseudoprogression that pushes the limits of immune-related response criteria and challenges the boundaries and definitions set by trial protocols. A middle-aged man with conventional clear cell renal cell carcinoma (RCC) had received multiple prior systemic treatments including vascular endothelial growth factor receptor tyrosine kinase inhibitors, as well as multiple surgeries and radiotherapy treatments. He was eventually started on nivolumab-the anti-programmed death receptor-1 monoclonal antibody approved for the treatment of advanced RCC. Clinical deterioration was observed soon after a 100 mg dose of nivolumab, with onset of acute renal failure and declining performance status. Radiologic progression was documented in multiple sites including worsening tumor infiltration of his residual kidney. The patient was on palliative treatment and visited by the home hospice team in an end-of-life situation. The patient unexpectedly improved and went on to achieve a durable tumor response. The case is illustrative of an extreme manifestation of pseudoprogression, and impels us to probe the assumptions and controversies surrounding this phenomenon.

Developmental Hip Dysplasia Diagnosis at Three-dimensional US: A Multicenter Study.

Purpose To validate accuracy of diagnosis of developmental dysplasia of the hip (DDH) from geometric properties of acetabular shape extracted from three-dimensional (3D) ultrasonography (US). Materials and Methods In this retrospective multi-institutional study, 3D US was added to conventional two-dimensional (2D) US of 1728 infants (mean age, 67 days; age range, 3-238 days) evaluated for DDH from January 2013 to December 2016. Clinical diagnosis after more than 6 months follow-up was normal (n = 1347), borderline (Graf IIa, later normalizing spontaneously; n = 140) or dysplastic (Graf IIb or higher, n = 241). Custom software accessible through the institution's research portal automatically calculated indexes including 3D posterior and anterior alpha angle and osculating circle radius from hip surface models generated with less than 1 minute of user input. Logistic regression predicted clinical diagnosis (normal = 0, dysplastic = 1) from 3D indexes (ie, age and sex). Output represented probability of hip dysplasia from 0 to 1 (output: >0.9, dysplastic; 0.11-0.89, borderline; <0.1, normal). Software can be accessed through the research portal. Results Area under the receiver operating characteristic curve was equivalently high for 3D US indexes and 2D US alpha angle (0.996 vs 0.987). Three-dimensional US helped to correctly categorize 97.5% (235 of 241) dysplastic and 99.4% (1339 of 1347) normal hips. No dysplastic hips were categorized as normal. Correct diagnosis was provided at initial 3D US scan in 69.3% (97 of 140) of the studies diagnosed as borderline at initial 2D US scans. Conclusion Automatically calculated 3D indexes of acetabular shape performed equivalently to high-quality 2D US scans at tertiary medical centers to help diagnose DDH. Three-dimensional US reduced the number of borderline studies requiring follow-up imaging by over two-thirds.

Association between maternal mid-gestation vitamin D status and neonatal abdominal adiposity.

OBJECTIVES: Lower vitamin D status has been associated with adiposity in children through adults. However, the evidence of the impact of maternal vitamin-D status during pregnancy on offspring's adiposity is mixed. The objective of this study was to examine the associations between maternal vitamin-D [25(OH)D] status at mid-gestation and neonatal abdominal adipose tissue (AAT) compartments, particularly the deep subcutaneous adipose tissue linked with metabolic risk. METHODS: Participants (N = 292) were Asian mother-neonate pairs from the mother-offspring cohort, Growing Up in Singapore Towards healthy Outcomes. Neonates born at ≥34 weeks gestation with birth weight ≥2000 g had magnetic resonance imaging (MRI) within 2-weeks post-delivery. Maternal plasma glucose using an oral glucose tolerance test and 25(OH)D concentrations were measured. 25(OH)D status was categorized into inadequate (≤75.0 nmol/L) and sufficient (>75.0 nmol/L) groups. Neonatal AAT was classified into superficial (sSAT), deep subcutaneous (dSAT), and internal (IAT) adipose tissue compartments. RESULTS: Inverse linear correlations were observed between maternal 25(OH)D and both sSAT (r = -0.190, P = 0.001) and dSAT (r = -0.206, P < 0.001). Each 1 nmol/L increase in 25(OH)D was significantly associated with reductions in sSAT (ß = -0.14 (95% CI: -0.24, -0.04) ml, P = 0.006) and dSAT (ß = -0.04 (-0.06, -0.01) ml, P = 0.006). Compared to neonates of mothers with 25(OH)D sufficiency, neonates with maternal 25(OH)D inadequacy had higher sSAT (7.3 (2.1, 12.4) ml, P = 0.006), and dSAT (2.0 (0.6, 3.4) ml, P = 0.005) volumes, despite similar birth weight. In the subset of mothers without gestational diabetes, neonatal dSAT was also greater (1.7 (0.3, 3.1) ml, P = 0.019) in neonates with maternal 25(OH)-inadequacy. The associations with sSAT and dSAT persisted even after accounting for maternal glycemia (fasting and 2-h plasma glucose). CONCLUSIONS: Neonates of Asian mothers with mid-gestation 25(OH)D inadequacy have a higher abdominal subcutaneous adipose tissue volume, especially dSAT (which is metabolically similar to visceral adipose tissue in adults), even after accounting for maternal glucose levels in pregnancy.

Establishment of institutional diagnostic reference level for computed tomography with automated dose-tracking software.

INTRODUCTION: The aim of this study was to establish institutional diagnostic reference levels (DRLs) by summarising doses collected across the five computed tomography (CT) system in our institution. METHODS: CT dose data of 15940 patients were collected retrospectively from May 2015 to October 2015 in five institutional scanners. The mean, 75th percentile and 90th percentile of the dose spread were calculated according to anatomic region. The common CT examinations such as head, chest, combined abdomen/pelvis (A/P), and combined chest/abdomen/pelvis (C/A/P) were reviewed. Distribution of CT dose index (CTDIvol), dose-length product (DLP) and effective dose (ED) were extracted from the data for single-phasic and multiphasic examinations. RESULTS: The institutional DRL for our CT units were established as mean (50th percentile) of CTDIvol (mGy), DLP (mGy.cm) and ED (mSv) for single and multiphasic studies using the dose-tracking software. In single phasic examination, Head: (49.0 mGy), (978.0 mGy.cm), (2.4 mSv) respectively; Chest: (6.0 mGy), (254.0 mGy.cm), (4.9 mSv) respectively; CT A/P (10.0 mGy), (514.0 mGy.cm), (8.9 mSv) respectively; CT C/A/P (10.0 mGy), (674.0 mGy.cm), (11.8 mSv) respectively. In multiphasic studies: Head (45.0 mGy), (1822.0 mGy.cm), (5.0 mSv) respectively; Chest (8.0 mGy), (577.0 mGy.cm), (10.0 mSv) respectively; CT A/P: (10.0 mGy), (1153.0 mGy.cm), (20.2 mSv) respectively; CT C/A/P: (11.0 mGy), (1090.0 mGy.cm), (19.2 mSv) respectively. CONCLUSIONS: The reported metrics offer a variety of information that institutions can use for quality improvement activities. The variations in dose between scanners suggest a large potential for optimisation of radiation dose.

Abdominal adipose tissue compartments vary with ethnicity in Asian neonates: Growing Up in Singapore Toward Healthy Outcomes birth cohort study.

BACKGROUND: A susceptibility to metabolic diseases is associated with abdominal adipose tissue distribution and varies between ethnic groups. The distribution of abdominal adipose tissue at birth may give insights into whether ethnicity-associated variations in metabolic risk originate partly in utero. OBJECTIVE: We assessed the influence of ethnicity on abdominal adipose tissue compartments in Asian neonates in the Growing Up in Singapore Toward Healthy Outcomes mother-offspring cohort. DESIGN: MRI was performed at ≤2 wk after birth in 333 neonates born at ≥34 wk of gestation and with birth weights ≥2000 g. Abdominal superficial subcutaneous tissue (sSAT), deep subcutaneous tissue (dSAT), and internal adipose tissue (IAT) compartment volumes (absolute and as a percentage of the total abdominal volume) were quantified. RESULTS: In multivariate analyses that were controlled for sex, age, and parity, the absolute and percentage of dSAT and the percentage of sSAT (but not absolute sSAT) were greater, whereas absolute IAT (but not the percentage of IAT) was lower, in Indian neonates than in Chinese neonates. Compared with Chinese neonates, Malay neonates had greater percentages of sSAT and dSAT but similar percentages of IAT. Marginal structural model analyses largely confirmed the results on the basis of volume percentages with controlled direct effects of ethnicity on abdominal adipose tissue; dSAT was significantly greater (1.45 mL; 95% CI: 0.49, 2.41 mL, P = 0.003) in non-Chinese (Indian or Malay) neonates than in Chinese neonates. However, ethnic differences in sSAT and IAT were NS [3.06 mL (95% CI:-0.27, 6.39 mL; P = 0.0712) for sSAT and -1.30 mL (95% CI: -2.64, 0.04 mL; P = 0.057) for IAT in non-Chinese compared with Chinese neonates, respectively]. CONCLUSIONS: Indian and Malay neonates have a greater dSAT volume than do Chinese neonates. This finding supports the notion that in utero influences may contribute to higher cardiometabolic risk observed in Indian and Malay persons in our population. If such differences persist in the longitudinal tracking of adipose tissue growth, these differences may contribute to the ethnic disparities in risks of cardiometabolic diseases. This trial was registered at clinicaltrials.gov as NCT01174875.

Sonography of the Pediatric Chest.

Traditionally, pediatric chest diseases are evaluated with chest radiography. Due to advancements in technology, the use of sonography has broadened. It has now become an established radiation-free imaging tool that may supplement plain-film findings and, in certain cases, the first-line modality for evaluation of the pediatric chest. This pictorial essay will demonstrate the diagnostic potential of sonography, review a spectrum of pediatric chest conditions, and discuss their imaging features and clinical importance.

The role of digestive factors in determining glycemic response in a multiethnic Asian population.

PURPOSE: There are wide inter-individual differences in glycemic response (GR). We aimed to examine key digestive parameters that influence inter-individual and ethnic differences in GR in healthy Asian individuals. METHODS: Seventy-five healthy male subjects (25 Chinese, 25 Malays, and 25 Asian-Indians) were served equivalent available carbohydrate amounts (50 g) of jasmine rice (JR) and basmati rice (BR) on separate occasions. Postprandial blood glucose concentrations were measured at fasting (-5 and 0 min) and at 15- to 30-min interval over 180 min. Mastication parameters (number of chews per mouth and chewing time per mouthful), saliva α-amylase activity, AMY1 gene copy numbers and gastric emptying rate were measured to investigate their relationships with GR. RESULTS: The GR for jasmine rice was significantly higher than for basmati rice (P < 0.001). The median number of AMY1 gene copies was 6, with a range of 2-15. There was a significant positive relationship between AMY1 copy number and α-amylase activity (P = 0.002). There were no significant ethnic differences in GR. For both rice varieties, the number of chews per mouthful was positively associated with the GR (JR, P = 0.011; BR, P = 0.005), while chewing time per mouthful showed a negative association (JR, P = 0.039; BR, P = 0.016). Ethnicity, salivary α-amylase activity, particle size distribution, gastric emptying rate and AMY1 gene copy numbers were not significant contributors to GR (P > 0.05). CONCLUSION: Mastication parameters contribute significantly to GR. Eating slowly and having larger food boluses before swallowing (less chewing), both potentially modifiable, may be beneficial in glycemic control.