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Measuring Maximum Head Circumference Within the Picture Archiving and Communication System: A Fully Automatic Approach.

ABSTRACT: This study describes an automatic technique to accurately determine the maximum head circumference (MHC) measurement from MRI studies within the Picture Archiving and Communications System, and can automatically add this measurement to the final radiology report. Participants were selected through a retrospective chart review of patients referred to the neurosurgery clinic. Forty-nine pediatric patients with ages ranging from 5 months to 11 years were included in the study. We created 14 printed ring structures to mirror the head circumference values at various ages along the x-axis of the Nellhaus chart. The 3D-printed structures were used to create MRI phantoms. Analytical obtainment of circumference values from the 3D objects and phantom images allowed for a fair estimation and correction of errors on the image-based-measuring instrument. Then, standard manual MHC measurements were performed and compared to values obtained from the patients' MRI T1 images using the tuned instrument proposed in this document. A T-test revealed no statistical difference between the manual assessments and the ones obtained by the automation p = 0.357, α = 0.05. This automatic application augments the more error-prone manual MHC measurement, and can add a numerical value to the final radiology report as a standard application.

SUBMITTER: Yepes-Calderon F

PROVIDER: S-EPMC8326831 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Medical education in China is in a transitional period, from passive learning models to experiential education. We modified an experiential education method for radiology education. The aim of this study is to evaluate the effect of this method on undergraduate radiology education. METHOD:With the help of the picture archiving and communication system (PACS) and RadiAnt DICOM Viewer, we modified an experiential education method that simulates similar working conditions for undergraduate medical students to formulate radiology diagnosis similar to clinical radiologists. A total of 101 students were allocated into either the experiential education group or the control group. The final examination scores and a 5-point Likert scale self-assessment questionnaire of radiologic skills were collected from all the students as an objective assessment and a subjective assessment respectively. A questionnaire was also used to assess the satisfaction with the experiential model in the experiential education group. Mann-Whitney U test was used to compare the ranked data, and t-tests were used to compare the numeric data. RESULTS:The experiential education group demonstrated significantly higher scores (7.4?±?1.3) compared to the control group (6.7?±?1.5, p?<?0.05) in the question type "description and diagnosis". The self-assessment questionnaire indicated that the experiential education was related to increased familiarity with the diagnosis thinking principle and the sequences and reconstruction methods of computer tomography (CT) imaging, which also strengthen participants' self-confidence to perform future clinical work (p?<?0.05). The self-assessment questionnaire in the experiential education group showed that the majority of students were satisfied with the organization (82.5%), interactivity (85%) and quality (85%) of the learning activity. Most students found this model of learning to be helpful for studying radiology (85%) and for understanding anatomy (90%). CONCLUSION:Compared with the traditional radiology education approach, the experiential education method showed greater efficacy in improving students' analysis and diagnostic skills and their self-confidence.

Project description:PurposePersonalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient's medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction.Materials and methodsAn algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations.ResultsUNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study.ConclusionIntegration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.

Project description:PurposeMeaningful changes in picture naming responses may be obscured when measuring accuracy instead of quality. A statistic that incorporates information about the severity and nature of impairments may be more sensitive to the effects of treatment.MethodWe analyzed data from repeated administrations of a naming test to 72 participants with stroke aphasia in a clinical trial for anomia therapy. Participants were divided into two groups for analysis to demonstrate replicability. We assessed reliability among response type scores from five raters. We then derived four summary statistics of naming ability and their changes over time for each participant: (a) the standard accuracy measure, (b) an accuracy measure adjusted for item difficulty, (c) an accuracy measure adjusted for item difficulty for specific response types, and (d) a distance measure adjusted for item difficulty for specific response types. While accuracy measures address the likelihood of a correct response, the distance measure reflects that different response types range in their similarity to the target. Model fit was assessed. The frequency of significant improvements and the average magnitude of improvements for each summary statistic were compared between treatment groups and a control group. Effect sizes for each model-based statistic were compared with the effect size for the standard accuracy measure.ResultsInterrater and intrarater reliability were near perfect, on average, though compromised somewhat by phonological-level errors. The effects of treatment were more evident, in terms of both frequency and magnitude, when using the distance measure versus the other accuracy statistics.ConclusionsConsideration of item difficulty and response types revealed additional effects of treatment on naming scores beyond those observed for the standard accuracy measure. The results support theories that assume naming ability is decomposable into subabilities rather than being monolithic, suggesting new opportunities for measuring treatment outcomes.Supplemental materialhttps://doi.org/10.23641/asha.17019515.

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Measuring Maximum Head Circumference Within the Picture Archiving and Communication System: A Fully Automatic Approach.

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