Notes

Identifying psychosocial determinants water, sterilization, as well as cleanliness (Clean) behaviors for the development of evidence-based Child Clean surgery (REDUCE system).
As academic centers partner and establish healthcare systems with community hospitals, delivery of subspecialty, multidisciplinary care in community hospital settings remains a challenge. Improving outcomes for central nervous system (CNS) disease is related to integrated care between neurosurgery (NS) and radiation oncology (RadOnc) specialties. Our multidisciplinary community hospital-based clinic, RADIANS, previously reported high patient approval of simultaneous evaluation with NS and RadOnc physicians. ML390 clinical trial Three-year experience is now reported.

Prospectively collected clinical and demographic patient data over three years was done, and surveys administered. Descriptive statistics reported as mean and percentages for patient characteristics, diagnosis, treatment and outcomes.

Between August 2016 and August 2019, 101 patients were evaluated. Mean age and distanced traveled was 61.2 years, and 54.9 miles, respectively. Patient Satisfaction Score was 4.79 (0-5 Scale, 5-very satisfied). Most common referralinary community hospital-based CNS clinic model is first of its kind to be reported, continuing strong patient approval at extended follow-up. Data indicates the model serves as a regional referral center, delivering evidence-based treatment modalities for complex CNS disease in community hospital settings, yielding high rates of local control and low rates of grade 3 or 4 radiation-induced toxicity.In 2020, the largest U.S. health care payer, the Centers for Medicare & Medicaid Services (CMS), established payment for artificial intelligence (AI) through two different systems in the Medicare Physician Fee Schedule (MPFS) and the Inpatient Prospective Payment System (IPPS). Within the MPFS, a new Current Procedural Terminology code was valued for an AI tool for diagnosis of diabetic retinopathy, IDx-RX. In the IPPS, Medicare established a New Technology Add-on Payment for Viz.ai software, an AI algorithm that facilitates diagnosis and treatment of large-vessel occlusion strokes. This article describes reimbursement in these two payment systems and proposes future payment pathways for AI. Keywords Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.About 50%-80% of very preterm infants (VPIs) (≤ 32 weeks gestational age) exhibit diffuse white matter abnormality (DWMA) on their MR images at term-equivalent age. It remains unknown if DWMA is associated with developmental impairments, and further study is warranted. To aid in the assessment of DWMA, a deep learning model for DWMA quantification on T2-weighted MR images was developed. This secondary analysis of prospective data was performed with an internal cohort of 98 VPIs (data collected from December 2014 to April 2016) and an external cohort of 28 VPIs (data collected from January 2012 to August 2014) who had already undergone MRI at term-equivalent age. Ground truth DWMA regions were manually annotated by two human experts with the guidance of a prior published semiautomated algorithm. In a twofold cross-validation experiment using the internal cohort of 98 infants, the three-dimensional (3D) ResU-Net model accurately segmented DWMA with a Dice similarity coefficient of 0.907 ± 0.041 (standard deviation) and balanced accuracy of 96.0% ± 2.1, outperforming multiple peer deep learning models. The 3D ResU-Net model that was trained with the whole internal cohort (n = 98) was further tested on an independent external test cohort (n = 28) and achieved a Dice similarity coefficient of 0.877 ± 0.059 and balanced accuracy of 92.3% ± 3.9. The externally validated 3D ResU-Net deep learning model for accurately segmenting DWMA may facilitate the clinical diagnosis of DWMA in VPIs. Supplemental material is available for this article. Keywords Brain/Brain Stem, Convolutional Neural Network (CNN), MR-Imaging, Pediatrics, Segmentation, Supervised learning © RSNA, 2021.
To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying and assessing lesion severity in the cartilage, bone marrow, meniscus, and anterior cruciate ligament (ACL) in the knee, improving overall MRI interreader agreement.

This retrospective study was conducted on 1435 knee MRI studies (
= 294 patients; mean age, 43 years ± 15 [standard deviation]; 153 women) collected within three previous studies (from 2011 to 2014). All MRI studies were acquired using high-spatial-resolution three-dimensional fast-spin-echo CUBE sequence. Three-dimensional convolutional neural networks were developed to detect the regions of interest within MRI studies and grade abnormalities of the cartilage, bone marrow, menisci, and ACL. Evaluation included sensitivity, specificity, and Cohen linear-weighted ĸ. The impact of AI-aided grading in intergrader agreement was assessed on an external dataset.

Binary lesion sensitivity reported for all tissues was between 70% and 88%. Specificity rangedable for this article. Keywords Bone Marrow, Cartilage, Computer Aided Diagnosis (CAD), Computer Applications-3D, Computer Applications-Detection/Diagnosis, Knee, Ligaments, MR-Imaging, Neural Networks, Observer Performance, Segmentation, Statistics © RSNA, 2021See also the commentary by Li and Chang in this issue. An earlier incorrect version of this article appeared online. This article was corrected on April 16, 2021.There is growing interest in integrating piezoelectric materials with complementary metal-oxide-semiconductor (CMOS) technology to enable expanded applications. A promising material for ultrasound transducer applications is polyvinylidene fluoride (PVDF), a piezoelectric polymer. One of the challenges with PVDF is that its piezoelectric properties can deteriorate when exposed to temperatures in excess of 70 °C for extended periods of time during fabrication. Here, we report on the effects of both shortening annealing times and providing this heating non-uniformly, as is characteristic of some processing conditions, on the piezoelectric coefficient (d 33) of PVDF films for various thicknesses. In this case, no degradation in the d 33 was observed at temperatures below 100 °C for anneal times of under one minute when this heating is applied through one side of the film, making PVDF compatible with many bonding and photolithographic processing steps required for CMOS integration. More surprisingly, for one-sided heating to temperatures between 90 °C and 110 °C, we observed a transient enhancement of the d 33 by nearly 40% that lasted for several hours after these anneals.
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