A novel galactose electrochemical biosensor intended for point-of-care measurement of quantitative liver function using galactose single-point test.

Liver disease has emerged as a healthcare burden because of high hospitalization rates attributed both to steatohepatitis and to severe hepatic toxicity associated with changes of drug exposure. Early detection of hepatic insufficiency is critical to preventing long-term liver damage. The galactose single-point test is recommended by the US FDA as a sensitive means to quantify liver function, yet the conventional method used for quantitation of circulating galactose still relies on the standard colorimetric method, requiring time-consuming and labor-intensive processes, and is confined to the medical laboratory, thus limiting prevalence. To facilitate time- and cost-effective disease management particularly during a pandemic, a pocket-sized rapid quantitative device consisting of a biosensor and electrochemical detection has been developed. An in vitro validation study demonstrated that the coefficient of variation was less than 15% and deviations were between -4 and 14% in the range of 100-1500 µg/mL. The device presented good linear fit (correlation coefficient, r = 0.9750) over the range of 150-1150 µg/mL. Moreover, the device was found to be free from interference of common endogenous and exogenous substances, and deviated hematocrit, enabling a direct measurement of galactose in the whole blood without sample pre-treatment steps. The clinical validation comprising 118 subjects showed high concordance (r = 0.953) between the device and the conventional colorimetric assay. Thus, this novel miniaturized device is reliable and robust for routine assessment of quantitative liver function intended for follow-up of hepatectomy, drug dose adjustment, and screening for galactosemia, allowing timely and cost-effective clinical management of patients.

Omnidirectional beacon-localization using a catadioptric system.

We present a catadioptric beacon localization system that can provide mobile network nodes with omnidirectional situational awareness of neighboring nodes. In this system, a receiver composed of a hyperboloidal mirror and camera is used to estimate the azimuth, elevation, and range of an LED beacon. We provide a general framework for understanding the propagation of error in the angle-of-arrival estimation and then present an experimental realization of such a system. The situational awareness provided by the proposed system can enable the alignment of communication nodes in an optical wireless network, which may be particularly useful in addressing RF-denied environments.

Design of dual-link (wide- and narrow-beam) LED communication systems.

We explore the design of an LED-based communication system comprising two free space optical links: one narrow-beam (primary) link for bulk data transmission and one wide-beam (beacon) link for alignment and support of the narrow-beam link. Such a system combines the high throughput of a highly directional link with the robust insensitivity to pointing errors of a wider-beam link. We develop a modeling framework for this dual-link configuration and then use this framework to explore system tradeoffs in power, range, and achievable rates. The proposed design presents a low-cost, compact, robust means of communication at short- to medium-ranges, and calculations show that data rates on the order of Mb/s are achievable at hundreds of meters with only a few LEDs.

Resonant interactions between a Mollow triplet sideband and a strongly coupled cavity.

We demonstrate resonant coupling of a Mollow triplet sideband to an optical cavity in the strong coupling regime. We show that, in this regime, the resonant sideband is strongly enhanced relative to the detuned sideband. Furthermore, the linewidth of the Mollow sidebands exhibits a highly nonlinear pump power dependence when tuned across the cavity resonance due to strong resonant interactions with the cavity mode. We compare our results to calculations using the effective phonon master equation and show that the nonlinear linewidth behavior is caused by strong coherent interaction with the cavity mode that exists only when the Mollow sideband is near cavity resonance.

Universal detection and segmentation of lymph nodes in multi-parametric MRI.

PURPOSE: Reliable measurement of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies of the body plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Previous approaches do not adequately exploit the complementary sequences in mpMRI to universally detect and segment lymph nodes, and they have shown fairly limited performance. METHODS: We propose a computer-aided detection and segmentation pipeline to leverage the T2 fat-suppressed (T2FS) and diffusion-weighted imaging (DWI) series from a mpMRI study. The T2FS and DWI series in 38 studies (38 patients) were co-registered and blended together using a selective data augmentation technique, such that traits of both series were visible in the same volume. A mask RCNN model was subsequently trained for universal detection and segmentation of 3D LNs. RESULTS: Experiments on 18 test mpMRI studies revealed that the proposed pipeline achieved a precision of [Formula: see text]%, sensitivity of [Formula: see text]% at 4 false positives (FP) per volume, and dice score of [Formula: see text]%. This represented an improvement of [Formula: see text]% in precision, [Formula: see text]% in sensitivity at 4 FP/volume, and [Formula: see text]% in dice score, respectively, over current approaches evaluated on the same dataset. CONCLUSION: Our pipeline universally detected and segmented both metastatic and non-metastatic nodes in mpMRI studies. At test time, the input data used by the trained model could either be the T2FS series alone or a blend of co-registered T2FS and DWI series. Contrary to prior work, this eliminated the reliance on both the T2FS and DWI series in a mpMRI study.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.

Automated detection of incidental abdominal aortic aneurysms on computed tomography.

PURPOSE: To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software. MATERIALS AND METHODS: The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs-maximum axial aortic diameter greater than 3 cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA. RESULTS: The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p < 0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively. CONCLUSION: Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque.

Osteoporosis Treatment and Outcomes in Patients Undergoing Adult Spinal Deformity Surgery.

BACKGROUND: This study aims to evaluate the prevalence and treatment of osteoporosis in patients undergoing long spinal fusion for adult spinal deformity (ASD) and compare the impact of osteoporosis treatment on surgical and radiographic outcomes. METHODS: We conducted a retrospective study of adult patients aged ≥40 years who underwent thoracolumbar ASD surgery at a single academic center between 2015 and 2021. We recorded demographic information, procedural details, and pharmacologic treatments. Primary outcomes included preoperative and postoperative sagittal vertical axis, pelvic incidence-lumbar lordosis mismatch, and postoperative complications such as surgical site infection, pseudarthrosis, proximal junctional kyphosis (PJK), and proximal junctional failure. Patients with osteoporosis were compared to those without. RESULTS: Among 168 patients, the prevalence of osteoporosis was 28.6%. Osteoporotic patients were older and predominantly female. At the time of surgery, 70.8% of osteoporotic patients were receiving pharmacologic treatment. Preoperative pelvic incidence-lumbar lordosis mismatch and sagittal vertical axis did not differ significantly between osteoporotic and nonosteoporotic cohorts. Both cohorts showed similar postoperative improvements. The osteoporotic cohort had a higher rate of PJK (35.4% vs. 17.5%, p=0.01), but no significant difference in proximal junctional failure rates. No significant differences were found between monotherapy and combination therapy outcomes for osteoporotic patients. CONCLUSIONS: Osteoporotic patients undergoing ASD surgery exhibited similar surgical outcomes and alignment improvements compared to nonosteoporotic patients, despite a higher rate of PJK. Pharmacological treatment appears beneficial in managing osteoporosis-related surgical risks. These findings highlight the importance of identifying and treating osteoporosis in ASD patients to minimize complications.

Inhibition of HIV-1 release by ADAM metalloproteinase inhibitors.

HIV-1 gp120 glycan binding to C-type lectin adhesion receptor L-selectin/CD62L on CD4 T cells facilitates viral attachment and entry. Paradoxically, the adhesion receptor impedes HIV-1 budding from infected T cells and the viral release requires the shedding of CD62L. To systematically investigate CD62L-shedding mediated viral release and its potential inhibition, we screened compounds specific for serine-, cysteine-, aspartyl-, and Zn-dependent proteases for CD62L shedding inhibition and found that a subclass of Zn-metalloproteinase inhibitors, including BB-94, TAPI, prinomastat, GM6001, and GI25423X, suppressed CD62L shedding. Their inhibition of HIV-1 infections correlated with enzymatic suppression of both ADAM10 and 17 activities and expressions of these ADAMs were transiently induced during the viral infection. These metalloproteinase inhibitors are distinct from the current antiretroviral drug compounds. Using immunogold labeling of CD62L, we observed association between budding HIV-1 virions and CD62L by transmission electron microscope, and the extent of CD62L-tethering of budding virions increased when the receptor shedding is inhibited. Finally, these CD62L shedding inhibitors suppressed the release of HIV-1 virions by CD4 T cells of infected individuals and their virion release inhibitions correlated with their CD62L shedding inhibitions. Our finding reveals a new therapeutic approach targeted at HIV-1 viral release.

Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% -> 47.8%, 74.2% -> 76.0%, and 26.4% -> 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% -> 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.

Improving segmentation and detection of lesions in CT scans using intensity distribution supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% → 47.8%, 74.2% → 76.0%, and 26.4% → 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% → 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.

Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.

Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation is to reduce the region of interest and inspect it at a higher sensitivity rather than performing it for the entire region. It is usually implemented as sequential or joint segmentation of organ and lesion, which requires additional supervision on organ segmentation. Instead, we propose to utilize an intensity distribution of a target lesion at no additional labeling cost to effectively separate regions where the lesions are possibly located from the background. It is incorporated into network training as an auxiliary task. We applied the proposed method to segmentation of small bowel carcinoid tumors in CT scans. We observed improvements for all metrics (33.5% → 38.2%, 41.3% → 47.8%, 30.0% → 35.9% for the global, per case, and per tumor Dice scores, respectively.) compared to the baseline method, which proves the validity of our idea. Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.

Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.

BACKGROUND: Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician awareness. Computed tomography (CT) is the most common imaging study for diagnosis of small bowel carcinoid tumor. It is often used with positron emission tomography (PET) to capture anatomical and functional aspects of carcinoid tumors and thus to increase the sensitivity. PURPOSE: We compared three different kinds of methods for the automatic detection of small bowel carcinoid tumors on CT scans, which is the first to the best of our knowledge. METHODS: Thirty-three preoperative CT scans of 33 unique patients with surgically-proven carcinoid tumors within the small bowel were collected. Ground-truth segmentation of tumors was drawn on CT scans by referring to available 18 F-DOPA PET scans and the corresponding radiology report. These scans were split into the trainval set (n = 24) and the test positive set (n= 9). Additionally, 22 CT scans of 22 unique patients who had no evidence of the tumor were collected to comprise the test negative set. We compared three different kinds of detection methods, which are detection network, patch-based classification, and segmentation-based methods. We also investigated the usefulness of small bowel segmentation for reduction of false positives (FPs) for each method. Free-response receiver operating characteristic (FROC) curves and receiver operating characteristic (ROC) curves were used for lesion- and patient-level evaluations, respectively. Statistical analyses comparing the FROC and ROC curves were also performed. RESULTS: The detection network method performed the best among the compared methods. For lesion-level detection, the detection network method, without the small bowel segmentation-based filtering, achieved sensitivity values of (60.8%, 81.1%, 82.4%, 86.5%) at per-scan FP rates of (1, 2, 4 ,8), respectively. The use of the small bowel segmentation did not improve the performance ( p = 0.742 $p=0.742$ ). For patient-level detection, again the detection network method, but with the small bowel segmentation-based filtering, achieved the highest AUC of 0.86 with a sensitivity of 78% and specificity of 82% at the Youden point. CONCLUSIONS: The carcinoid tumors in this patient population were very small and potentially difficult to diagnose. The presented method showed reasonable sensitivity at small numbers of FPs for lesion-level detection. It also achieved a promising AUC for patient-level detection. The method may have clinical application in patients with this rare and difficult to detect disease.

Universal lymph node detection in T2 MRI using neural networks.

PURPOSE: Identification of lymph nodes (LNs) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) is critical for assessment of lymphadenopathy. Prior work on LN detection has been limited to specific anatomical regions of the body (pelvis, rectum). Therefore, an approach to universally detect both benign and metastatic nodes in T2 MRI studies of the body is highly desirable. METHODS: We developed a Computer Aided Detection (CAD) pipeline to universally identify LN in T2 MRI. First, we trained various neural networks for detecting LN: Faster RCNN with and without Hard Negative Example Mining (HNEM), FCOS, FoveaBox, VFNet, and Detection Transformer (DETR). Next, we show that VFNet with Adaptive Training Sample Selection (ATSS) outperformed Faster RCNN with HNEM. Finally, we ensembled models that surpassed a 45% mAP threshold. RESULTS: Experiments on 122 test studies revealed that VFNet achieved a 51.1% mAP and 78.7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52.3% and sensitivity of 78.7% at 4FP. We found that VFNet and the one-stage model ensemble can be interchangeably used in the CAD pipeline. CONCLUSION: Our CAD pipeline universally detected both benign and metastatic nodes in T2 MRI studies, resulting in a sensitivity improvement of [Formula: see text]14% over the current LN detection approaches (sensitivity of 78.7% at 4 FP vs. 64.6% at 5 FP per volume).

Radiologic reporting of MRI-proven thoracolumbar epidural metastases on body CT: 12-Year single-institution experience.

RATIONALE AND OBJECTIVES: Metastatic epidural masses are an important radiological finding. The purpose of this study is to determine factors associated with non-reporting of thoracolumbar epidural metastases on body CT. MATERIALS AND METHODS: In a study population of 166 patients from a single institution over a 12-year period, 293 body CT examinations were identified which were performed within 30 days before or after a spine MRI diagnosis of epidural metastasis. Associations were sought between patient diagnosis, CT examination characteristics, reporting radiologist (n = 17), and lesion characteristics with respect to whether an epidural metastasis was reported on CT. RESULTS: In retrospective consensus review comprised of 3 radiologists, epidural metastases reported on spine MRI were clearly visible in 80.5% (236/293) of body CT examinations, however 65.3% (154/236) of the body CT reports omitted reporting their presence, even in cases where there was a preceding MRI diagnosis within 30 days (65.4%, 74/113). The identity of the reporting radiologist was statistically significantly associated with the accurate diagnostic reporting of epidural metastasis on body CT (p = 0.04). The only lesion features which were statistically significantly associated with CT reporting were lesion volume (p = 0.03) on noncontrast CT, and lesion volume (p = 0.006) and percentage of spinal canal stenosis (p = 0.001) on intravenous contrast-enhanced CT. The presence or absence of intravenous contrast was not significantly associated with CT reporting (p = 1.0). CONCLUSION: Using spine MRI as the reference standard for the presence of epidural tumor, the majority of body CT reports omit describing thoracolumbar epidural metastases which are clearly visible in retrospect.

HIV-1 release requires Nef-induced caspase activation.

HIV infection remains incurable to date and there are no compounds targeted at the viral release. We show here HIV viral release is not spontaneous, rather requires caspases activation and shedding of its adhesion receptor, CD62L. Blocking the caspases activation caused virion tethering by CD62L and the release of deficient viruses. Not only productive experimental HIV infections require caspases activation for viral release, HIV release from both viremic and aviremic patient-derived CD4 T cells also require caspase activation, suggesting HIV release from cellular viral reservoirs depends on apoptotic shedding of the adhesion receptor. Further transcriptomic analysis of HIV infected CD4 T cells showed a direct contribution of HIV accessory gene Nef to apoptotic caspases activation. Current HIV cure focuses on the elimination of latent cellular HIV reservoirs that are resistant to infection-induced cell death. This has led to therapeutic strategies to stimulate T cell apoptosis in a "kick and kill" approach. Our current work has shifted the paradigm on HIV-induced apoptosis and suggests such approach would risk to induce HIV release and thus be counter-productive. Instead, our study supports targeting of viral reservoir release by inhibiting of caspases activation.

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.

How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.

Factors Associated With Total Discharge Opioid Prescription Morphine Milligram Equivalent Amounts Following Primary Anterior Cervical Spine Surgery.

BACKGROUND: Opioid overuse is a substantial cause of morbidity and mortality in the United States, and orthopaedic surgeons are the third highest prescribers of opioids. Postoperative prescribing patterns vary widely, and there is a paucity of data evaluating patient and surgical factors associated with discharge opioid prescribing patterns after elective anterior cervical surgery (ACS). The purpose of this study was to evaluate the volume of postoperative opioids prescribed and factors associated with discharge opioid prescription volumes after elective ACS. METHODS: We retrospectively identified patients aged 18 years and older who underwent elective primary anterior cervical diskectomy and fusion (ACDF), cervical disk arthroplasty (CDA), or hybrid procedure (ACDF and CDA at separate levels) at a single institution between 2015 and 2021. Demographic, surgical, and opioid prescription data were obtained from patients' electronic medical records. Univariate and multivariate analyses were conducted to assess for independent associations with discharge opioid volumes. RESULTS: A total of 313 patients met inclusion criteria, including 226 (72.2%) ACDF, 69 (22.0%) CDA, and 18 (5.8%) hybrid procedure patients. Indications included radiculopathy in 63.6%, myelopathy in 19.2%, and myeloradiculopathy in 16.3%. The average age was 57.2 years, and 50.2% of patients were male. Of these, 88 (28.1%) underwent one-level, 137 (43.8%) underwent two-level, 83 (26.5%) underwent three-level, and 5 (1.6%) underwent four-level surgery. Younger age (P = 0.010), preoperative radiculopathy (P = 0.029), procedure type (ACDF, P < 0.001), preoperative opioid use (P = 0.012), and discharge prescription written by a midlevel provider (P = 0.010) were independently associated with greater discharge opioid prescription volumes. CONCLUSION: We identified wide variability in prescription opioid discharge volumes after ACS and patient, procedure, and perioperative factors associated with greater discharge opioid volumes. These factors should be considered when designing protocols and interventions to reduce and optimize postoperative opioid use after ACS.

Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study.

Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples, teaching a machine to learn from such a "long-tailed" distribution is much more difficult, as standard methods would be easily biased toward the most frequent classes. In this paper, we present a comprehensive benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. To accomplish this, we introduce a challenging new long-tailed chest X-ray benchmark to facilitate research on developing long-tailed learning methods for medical image classification. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images. We evaluate both standard and state-of-the-art long-tailed learning methods on this new benchmark, analyzing which aspects of these methods are most beneficial for long-tailed medical image classification and summarizing insights for future algorithm design. The datasets, trained models, and code are available at https://github.com/VITA-Group/LongTailCXR.