Indiana University Health University Hospital is a teaching hospital in Indianapolis, Indiana, United States, affiliated with the Indiana University School of Medicine and Indiana University Health.With nearly 1,100 physician faculty members at Indiana University Health University Hospital, physicians, surgeons, nurses and staff care for more than 57,000 patients a year.[citation needed] Approximately 52 percent of physicians in Indiana were trained at Indiana University Health University Hospital.[citation needed] In addition, Indiana University Health University Hospital physicians and staff continuously seek advances in medicine. The staff actively participate in approximately 150 clinical and prevention trials to provide optimal patient treatments.[citation needed] As part of Indiana University Health, the hospital works closely with nearby Indiana University Health Methodist Hospital and Riley Hospital for Children at Indiana University Health.The Indiana University Health University Hospital Emergency Department closed on June 30, 2014, with its adult emergency room care services moving to the Indiana University Health Methodist Hospital Emergency Medicine and Trauma Center.
Introduction Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2 DLR ) and T1 TSE (T1 DLR ) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence. Material and methods This retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale. Results Accelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging. Conclusion In conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.
Achieving high-fidelity 3D surface reconstruction while preserving fine details remains challenging, especially in the presence of materials with complex reflectance properties and without a dense-view setup. In this paper, we introduce a versatile framework that incorporates multi-view normal and optionally reflectance maps into radiance-based surface reconstruction. Our approach employs a pixel-wise joint re-parametrisation of reflectance and surface normals, representing them as a vector of radiances under simulated, varying illumination. This formulation enables seamless incorporation into standard surface reconstruction pipelines, such as traditional multi-view stereo (MVS) frameworks or modern neural volume rendering (NVR) ones. Combined with the latter, our approach achieves state-of-the-art performance on multi-view photometric stereo (MVPS) benchmark datasets, including DiLiGenT-MV, LUCES-MV and Skoltech3D. In particular, our method excels in reconstructing fine-grained details and handling challenging visibility conditions. The present paper is an extended version of the earlier conference paper by Brument et al (in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024), featuring an accelerated and more robust algorithm as well as a broader empirical evaluation. The code and data relative to this article are available at https://github.com/RobinBruneau/RNb-NeuS2 .
We present what is, to our knowledge, the first reported case of concurrent bullous pemphigoid (BP) and antibody-mediated rejection (AMR) in a lung transplant patient. Similar associations have been described in other solid organ transplant settings, most notably in renal transplantation, where explantation of the allograft has frequently been followed by resolution of the skin disease. These observations suggest a broader potential connection between autoimmune blistering disorders and allograft rejection mechanisms, especially as emerging evidence highlights the role of non-HLA antibodies. A comprehensive review of previously reported cases of BP in solid organ transplant recipients is therefore warranted to place this case in context and to assess its significance within the existing literature.
Wearable cameras provide a means to assess hand function in individuals with spinal cord injury (SCI) beyond clinical settings. Previous studies have found that clinicians acknowledge the potential of egocentric video to monitor and inform rehabilitation. Nonetheless, the need for time-intensive manual review of the footage remains a challenge to its integration into clinical practice. To address this barrier, we investigated the utility of video summarization for egocentric videos of hand use after SCI. A dataset comprising 316 egocentric videos from 20 individuals with cervical SCI was used. Individuals wore head-mounted cameras to record daily activities in their home. Three unsupervised video summarization algorithms were applied: DR-DSN (reinforcement learning), CTVSUM (contrastive learning), and CA-SUM (attention-based learning). The resulting summaries were manually evaluated on a subset of five videos (each summarized by all three algorithms) by 15 participants using five criteria rated on a 5-point Likert scale: (C1) inclusion of hand movements, (C2) visibility of difficulties and compensation, (C3) contextual clarity, (C4) depiction of hand function, and (C5) preservation of key information. Additionally, summaries were assessed using computational metrics: coverage, temporal distribution, diversity, and representativeness. An average manual rating of 3.7 ± 1.2 was observed. Ratings differed significantly across both evaluation criteria (F = 13.69, p < 0.001, η^2 = 0.167) and algorithms (F = 24.00, p < 0.001, η^2 = 0.103). In particular, summaries were rated higher for C3 and lower for C2, while CA-SUM consistently received the highest scores. Among the computational metrics, diversity showed a strong negative association with manual ratings (b = −4.8, p = 0.032, R2 = 0.827), while representativeness was positively associated (b = 17.8, p = 0.047, R2 = 0.779). All three algorithms produced adequate video summaries that captured essential content. However, enhancing the depiction of aspects such as functional difficulties and compensatory strategies could further improve the clinical value of the summaries. Moreover, discrepancies between computational and manual evaluations highlight the need to train algorithms on more human-centered criteria. Overall, this work demonstrates the potential of automatic video summarization to support the integration of wearable cameras into outpatient SCI rehabilitation.
