Generative Retrieval Does Not Improve Long-Term Retention of Regional Anesthesia Ultrasound Anatomy in Unengaged Learners.

BACKGROUND: Ultrasound-guided regional anesthesia is increasingly used in the perioperative period but performance requires a mastery of regional ultrasound anatomy. We aimed to study whether the use of generative retrieval to learn ultrasound anatomy would improve long-term recall. METHODS: Fourth-year medical students without prior training in ultrasound techniques were randomized into standard practice (SP) and generative retrieval (GR) groups. An initial pre-test consisted of 74 regional anesthesia ultrasound images testing common anatomic structures. During the study/learning session, GR participants were required to verbally identify an unlabeled anatomical structure within 10 seconds of the ultrasound image appearing on the screen. A labeled image of the structure was then shown to the GR participant for 5 seconds. SP participants viewed the same ultrasound images labeled with the correct anatomical structure for 15 seconds. Retention was tested at 1 week and 1 month following the study session. Participants completed a satisfaction survey after each session. RESULTS: Forty-five medical students were enrolled with forty included in the analysis. There was no statistically significant difference in baseline scores (GR = 11.5 ± 4.9; SP = 11.2 ± 6.2; P = 0.84). There was no difference in scores at both the 1-week (SP = 54.5 ± 13.3; GR = 53.9 ± 10.5; P = 0.88) and 1-month (SP = 54.0 ± 14.5; GR = 50.7 ± 11.1; P = 0.42) time points. There was no statistically significant difference in learner satisfaction metrics between the groups. CONCLUSIONS: The use of generative retrieval practice to learn regional anesthesia ultrasound anatomy did not yield significant differences in learning and retention compared with standard learning.

What's in a Name? Accurately Diagnosing Metopic Craniosynostosis Using a Computational Approach.

BACKGROUND: The metopic suture is unlike other cranial sutures in that it normally closes in infancy. Consequently, the diagnosis of metopic synostosis depends primarily on a subjective assessment of cranial shape. The purpose of this study was to create a simple, reproducible radiographic method to quantify forehead shape and distinguish trigonocephaly from normal cranial shape variation. METHODS: Computed tomography scans were acquired for 92 control patients (mean age, 4.2 ± 3.3 months) and 18 patients (mean age, 6.2 ± 3.3 months) with a diagnosis of metopic synostosis. A statistical model of the normal cranial shape was constructed, and deformation fields were calculated for patients with metopic synostosis. Optimal and divergence (simplified) interfrontal angles (IFA) were defined based on the three points of maximum average deformation on the frontal bones and metopic suture, respectively. Statistical analysis was performed to assess the accuracy and reliability of the diagnostic procedure. RESULTS: The optimal interfrontal angle was found to be significantly different between the synostosis (116.5 ± 5.8 degrees; minimum, 106.8 degrees; maximum, 126.6 degrees) and control (136.7 ± 6.2 degrees; minimum, 123.8 degrees; maximum, 169.3 degrees) groups (p < 0.001). Divergence interfrontal angles were also significantly different between groups. Accuracy, in terms of available clinical diagnosis, for the optimal and divergent angles, was 0.981 and 0.954, respectively. CONCLUSIONS: Cranial shape analysis provides an objective and extremely accurate measure by which to diagnose abnormal interfrontal narrowing, the hallmark of metopic synostosis. The simple planar angle measurement proposed is reproducible and accurate, and can eliminate diagnostic subjectivity in this disorder. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.

Quantitative Ultrasound for Measuring Obstructive Severity in Children with Hydronephrosis.

PURPOSE: We define sonographic biomarkers for hydronephrotic renal units that can predict the necessity of diuretic nuclear renography. MATERIALS AND METHODS: We selected a cohort of 50 consecutive patients with hydronephrosis of varying severity in whom 2-dimensional sonography and diuretic mercaptoacetyltriglycine renography had been performed. A total of 131 morphological parameters were computed using quantitative image analysis algorithms. Machine learning techniques were then applied to identify ultrasound based safety thresholds that agreed with the t½ for washout. A best fit model was then derived for each threshold level of t½ that would be clinically relevant at 20, 30 and 40 minutes. Receiver operating characteristic curve analysis was performed. Sensitivity, specificity and area under the receiver operating characteristic curve were determined. Improvement obtained by the quantitative imaging method compared to the Society for Fetal Urology grading system and the hydronephrosis index was statistically verified. RESULTS: For the 3 thresholds considered and at 100% sensitivity the specificities of the quantitative imaging method were 94%, 70% and 74%, respectively. Corresponding area under the receiver operating characteristic curve values were 0.98, 0.94 and 0.94, respectively. Improvement obtained by the quantitative imaging method over the Society for Fetal Urology grade and hydronephrosis index was statistically significant (p <0.05 in all cases). CONCLUSIONS: Quantitative imaging analysis of renal sonograms in children with hydronephrosis can identify thresholds of clinically significant washout times with 100% sensitivity to decrease the number of diuretic renograms in up to 62% of children.

Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis.

This paper introduces a complete framework for the quantification of renal structures (parenchyma, and collecting system) in 3D ultrasound (US) images. First, the segmentation of the kidney is performed using Gabor-based appearance models (GAM), a variant of the popular active shape models, properly tailored to the imaging physics of US image data. The framework also includes a new graph-cut based method for the segmentation of the collecting system, including brightness and contrast normalization, and positional prior information. The significant advantage (p = 0.03) of the new method over previous approaches in terms of segmentation accuracy has been successfully verified on clinical 3DUS data from pediatric cases with hydronephrosis. The promising results obtained in the estimation of the volumetric hydronephrosis index demonstrate the potential of our new framework to quantify anatomy in US and asses the severity of hydronephrosis.

Personalized assessment of craniosynostosis via statistical shape modeling.

We present a technique for the computational analysis of craniosynostosis from CT images. Our fully automatic methodology uses a statistical shape model to produce diagnostic features tailored to the anatomy of the subject. We propose a computational anatomy approach for measuring shape abnormality in terms of the closest case from a multi-atlas of normal cases. Although other authors have tackled malformation characterization for craniosynostosis in the past, our approach involves several novel contributions (automatic labeling of cranial regions via graph cuts, identification of the closest morphology to a subject using a multi-atlas of normal anatomy, detection of suture fusion, registration using masked regions and diagnosis via classification using quantitative measures of local shape and malformation). Using our automatic technique we obtained for each subject an index of cranial suture fusion, and deformation and curvature discrepancy averages across five cranial bones and six suture regions. Significant differences between normal and craniosynostotic cases were obtained using these characteristics. Machine learning achieved a 92.7% sensitivity and 98.9% specificity for diagnosing craniosynostosis automatically, values comparable to those achieved by trained radiologists. The probability of correctly classifying a new subject is 95.7%.

Automatic analysis of pediatric renal ultrasound using shape, anatomical and image acquisition priors.

In this paper we present a segmentation method for ultrasound (US) images of the pediatric kidney, a difficult and barely studied problem. Our method segments the kidney on 2D sagittal US images and relies on minimal user intervention and a combination of improvements made to the Active Shape Model (ASM) framework. Our contributions include particle swarm initialization and profile training with rotation correction. We also introduce our methodology for segmentation of the kidney's collecting system (CS), based on graph-cuts (GC) with intensity and positional priors. Our intensity model corrects for intensity bias by comparison with other biased versions of the most similar kidneys in the training set. We prove significant improvements (p < 0.001) with respect to classic ASM and GC for kidney and CS segmentation, respectively. We use our semi-automatic method to compute the hydronephrosis index (HI) with an average error of 2.67 +/- 5.22 percentage points similar to the error of manual HI between different operators of 2.31 +/- 4.54 percentage points.