Expected-value bias in routine third-trimester growth scans.

OBJECTIVES: Operators performing fetal growth scans are usually aware of the gestational age of the pregnancy, which may lead to expected-value bias when performing biometric measurements. We aimed to evaluate the incidence of expected-value bias in routine fetal growth scans and assess its impact on standard biometric measurements. METHODS: We collected prospectively full-length video recordings of routine ultrasound growth scans coupled with operator eye tracking. Expected value was defined as the gestational age at the time of the scan, based on the estimated due date that was established at the dating scan. Expected-value bias was defined as occurring when the operator looked at the measurement box on the screen during the process of caliper adjustment before saving a measurement. We studied the three standard biometric planes on which measurements of head circumference (HC), abdominal circumference (AC) and femur length (FL) are obtained. We evaluated the incidence of expected-value bias and quantified the impact of biased measurements. RESULTS: We analyzed 272 third-trimester growth scans, performed by 16 operators, during which a total of 1409 measurements (354 HC, 703 AC and 352 FL; including repeat measurements) were obtained. Expected-value bias occurred in 91.4% of the saved standard biometric plane measurements (85.0% for HC, 92.9% for AC and 94.9% for FL). The operators were more likely to adjust the measurements towards the expected value than away from it (47.7% vs 19.7% of measurements; P < 0.001). On average, measurements were corrected by 2.3 ± 5.6, 2.4 ± 10.4 and 3.2 ± 10.4 days of gestation towards the expected gestational age for the HC, AC, and FL measurements, respectively. Additionally, we noted a statistically significant reduction in measurement variance once the operator was biased (P = 0.026). Comparing the lowest and highest possible estimated fetal weight (using the smallest and largest biased HC, AC and FL measurements), we noted that the discordance, in percentage terms, was 10.1% ± 6.5%, and that in 17% (95% CI, 12-21%) of the scans, the fetus could be considered as small-for-gestational age or appropriate-for-gestational age if using the smallest or largest possible measurements, respectively. Similarly, in 13% (95% CI, 9-16%) of scans, the fetus could be considered as large-for-gestational age or appropriate-for-gestational age if using the largest or smallest possible measurements, respectively. CONCLUSIONS: During routine third-trimester growth scans, expected-value bias frequently occurs and significantly changes standard biometric measurements obtained. © 2019 the Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size

KIR haplotypes are associated with late-onset type 1 diabetes in European-American families.

Classical human leukocyte antigens (HLA) genes confer the strongest, but not the only, genetic susceptibility to type 1 diabetes. Killer cell immunoglobulin-like receptors (KIR), on natural killer (NK) cells, bind ligands including class I HLA. We examined presence or absence, with copy number, of KIR loci in 1698 individuals, from 339 multiplex type 1 diabetes families, from the Human Biological Data Interchange, previously genotyped for HLA. Combining family data with KIR copy number information allowed assignment of haplotypes using identity by descent. This is the first disease study to use KIR copy number typing and unambiguously define haplotypes by gene transmission. KIR A1 haplotypes were positively associated with T1D in the subset of patients without the high T1D risk HLA genotype, DR3/DR4 (odds ratio=1.29, P=0.0096). The data point to a role for KIR in type 1 diabetes risk in late-onset patients. In the top quartile (age of onset>14), KIR A2 haplotype was overtransmitted (63.4%, odds ratio=1.73, P=0.024) and KIR B haplotypes were undertransmitted (41.1%, odds ratio=0.70, P=0.0052) to patients. The data suggest that inhibitory 'A' haplotypes are predisposing and stimulatory 'B' haplotypes confer protection in both DR3/DR4-negative and late-onset patient groups.This work was supported in part by National Institutes of Health award R01 DK61722 (J.A.N.). Research in the Trowsdale lab is supported by the MRC and Wellcome Trust with part funding from the National Institute for Health Research Cambridge Biomedical Research Centre.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/gene.2015.4

Quality-improvement program for ultrasound-based fetal anatomy screening using large-scale clinical audit.

OBJECTIVE: A large-scale audit and peer review of ultrasound images may improve sonographer performance, but is rarely performed consistently as it is time-consuming and expensive. The aim of this study was to perform a large-scale audit of routine fetal anatomy scans to assess if a full clinical audit cycle can improve clinical image-acquisition standards. METHODS: A large-scale, clinical, retrospective audit was conducted of ultrasound images obtained during all routine anomaly scans performed from 18 + 0 to 22 + 6 weeks' gestation at a UK hospital during 2013 (Cycle 1), to build a baseline understanding of the performance of sonographers. Targeted actions were undertaken in response to the findings with the aim of improving departmental performance. A second full-year audit was then performed of fetal anatomy ultrasound images obtained during the following year (Cycle 2). An independent pool of experienced sonographers used an online tool to assess all scans in terms of two parameters: scan completeness (i.e. were all images archived?) and image quality using objective scoring (i.e. were images of high quality?). Both were assessed in each audit at the departmental level and at the individual sonographer level. A random sample of 10% of scans was used to assess interobserver reproducibility. RESULTS: In Cycle 1 of the audit, 103 501 ultrasound images from 6257 anomaly examinations performed by 22 sonographers were assessed; in Cycle 2, 153 557 images from 6406 scans performed by 25 sonographers were evaluated. The analysis was performed including the images obtained by the 20 sonographers who participated in both cycles. Departmental median scan completeness improved from 72% in the first year to 78% at the second assessment (P < 0.001); median image-quality score for all fetal views improved from 0.83 to 0.86 (P < 0.001). The improvement was greatest for those sonographers who performed poorest in the first audit; with regards to scan completeness, the poorest performing 15% of sonographers in Cycle 1 improved by more than 30 percentage points, and with regards to image quality, the poorest performing 11% in Cycle 1 showed a more than 10% improvement. Interobserver repeatability of scan completeness and image-quality scores across different fetal views were similar to those in the published literature. CONCLUSIONS: A clinical audit and a set of targeted actions helped improve sonographer scan-acquisition completeness and scan quality. Such adherence to recommended clinical acquisition standards may increase the likelihood of correct measurement and thereby fetal growth assessment, and should allow better detection of abnormalities. As such a large-scale audit is time consuming, further advantages would be achieved if this process could be automated. © 2018 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology

Spatio-Temporal Partitioning And Description Of Full-Length Routine Fetal Anomaly Ultrasound Scans

This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-l accuracy =0.77 and top-3 accuracy =0.94. Automated partitioning and characterisation on unlabelled full-length video scans show high correlation (ρ=0.95, p=0.0004) with workflow statistics of manually labelled videos, suggesting practicality of proposed methods

Transplanted astrocytes derived from BMP- or CNTF-treated glial-restricted precursors have opposite effects on recovery and allodynia after spinal cord injury

<p>Abstract</p> <p>Background</p> <p>Two critical challenges in developing cell-transplantation therapies for injured or diseased tissues are to identify optimal cells and harmful side effects. This is of particular concern in the case of spinal cord injury, where recent studies have shown that transplanted neuroepithelial stem cells can generate pain syndromes.</p> <p>Results</p> <p>We have previously shown that astrocytes derived from glial-restricted precursor cells (GRPs) treated with bone morphogenetic protein-4 (BMP-4) can promote robust axon regeneration and functional recovery when transplanted into rat spinal cord injuries. In contrast, we now show that transplantation of GRP-derived astrocytes (GDAs) generated by exposure to the gp130 agonist ciliary neurotrophic factor (GDAs^CNTF), the other major signaling pathway involved in astrogenesis, results in failure of axon regeneration and functional recovery. Moreover, transplantation of GDA^CNTFcells promoted the onset of mechanical allodynia and thermal hyperalgesia at 2 weeks after injury, an effect that persisted through 5 weeks post-injury. Delayed onset of similar neuropathic pain was also caused by transplantation of undifferentiated GRPs. In contrast, rats transplanted with GDAs^BMP did not exhibit pain syndromes.</p> <p>Conclusion</p> <p>Our results show that not all astrocytes derived from embryonic precursors are equally beneficial for spinal cord repair and they provide the first identification of a differentiated neural cell type that can cause pain syndromes on transplantation into the damaged spinal cord, emphasizing the importance of evaluating the capacity of candidate cells to cause allodynia before initiating clinical trials. They also confirm the particular promise of GDAs treated with bone morphogenetic protein for spinal cord injury repair.</p

Astrocytes derived from glial-restricted precursors promote spinal cord repair

BACKGROUND: Transplantation of embryonic stem or neural progenitor cells is an attractive strategy for repair of the injured central nervous system. Transplantation of these cells alone to acute spinal cord injuries has not, however, resulted in robust axon regeneration beyond the sites of injury. This may be due to progenitors differentiating to cell types that support axon growth poorly and/or their inability to modify the inhibitory environment of adult central nervous system (CNS) injuries. We reasoned therefore that pre-differentiation of embryonic neural precursors to astrocytes, which are thought to support axon growth in the injured immature CNS, would be more beneficial for CNS repair. RESULTS: Transplantation of astrocytes derived from embryonic glial-restricted precursors (GRPs) promoted robust axon growth and restoration of locomotor function after acute transection injuries of the adult rat spinal cord. Transplantation of GRP-derived astrocytes (GDAs) into dorsal column injuries promoted growth of over 60% of ascending dorsal column axons into the centers of the lesions, with 66% of these axons extending beyond the injury sites. Grid-walk analysis of GDA-transplanted rats with rubrospinal tract injuries revealed significant improvements in locomotor function. GDA transplantation also induced a striking realignment of injured tissue, suppressed initial scarring and rescued axotomized CNS neurons with cut axons from atrophy. In sharp contrast, undifferentiated GRPs failed to suppress scar formation or support axon growth and locomotor recovery. CONCLUSION: Pre-differentiation of glial precursors into GDAs before transplantation into spinal cord injuries leads to significantly improved outcomes over precursor cell transplantation, providing both a novel strategy and a highly effective new cell type for repairing CNS injuries

Plane Localization in 3-D Fetal Neurosonography for Longitudinal Analysis of the Developing Brain.

The parasagittal (PS) plane is a 2-D diagnostic plane used routinely in cranial ultrasonography of the neonatal brain. This paper develops a novel approach to find the PS plane in a 3-D fetal ultrasound scan to allow image-based biomarkers to be tracked from prebirth through the first weeks of postbirth life. We propose an accurate plane-finding solution based on regression forests (RF). The method initially localizes the fetal brain and its midline automatically. The midline on several axial slices is used to detect the midsagittal plane, which is used as a constraint in the proposed RF framework to detect the PS plane. The proposed learning algorithm guides the RF learning method in a novel way by: 1) using informative voxels and voxel informative strength as a weighting within the training stage objective function, and 2) introducing regularization of the RF by proposing a geometrical feature within the training stage. Results on clinical data indicate that the new automated method is more reproducible than manual plane finding obtained by two clinicians

Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.

INTRODUCTION: Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their cognitive workload, captured while they undertake routine fetal ultrasound examinations. Our experiments and analysis are performed on real-world datasets obtained using remote eye-tracking under natural clinical environmental conditions. METHODS: Our analysis pipeline involves careful temporal sequence (time-series) extraction by retrospectively matching the pupil diameter data with tasks captured in the corresponding ultrasound scan video in a multi-modal data acquisition setup. This is followed by the pupil diameter pre-processing and the calculation of pupillary response sequences. Exploratory statistical analysis of the operator pupillary responses and comparisons of the distributions between ultrasonographic tasks (fetal heart versus fetal brain) and operator expertise (newly-qualified versus experienced operators) are performed. Machine learning is explored to automatically classify the temporal sequences into the corresponding ultrasonographic tasks and operator experience using temporal, spectral, and time-frequency features with classical (shallow) models, and convolutional neural networks as deep learning models. RESULTS: Preliminary statistical analysis of the extracted pupillary response shows a significant variation for different ultrasonographic tasks and operator expertise, suggesting different extents of cognitive workload in each case, as measured by pupillometry. The best-performing machine learning models achieve receiver operating characteristic (ROC) area under curve (AUC) values of 0.98 and 0.80, for ultrasonographic task classification and operator experience classification, respectively. CONCLUSION: We conclude that we can successfully assess cognitive workload from pupil diameter changes measured while ultrasound operators perform routine scans. The machine learning allows the discrimination of the undertaken ultrasonographic tasks and scanning expertise using the pupillary response sequences as an index of the operators' cognitive workload. A high cognitive workload can reduce operator efficiency and constrain their decision-making, hence, the ability to objectively assess cognitive workload is a first step towards understanding these effects on operator performance in biomedical applications such as medical imaging