Bentley Alvarez (waystem49)

The referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative. To develop a machine-learning-based algorithm which accurately identifies patients as candidates for consultation with a spine surgeon, using only magnetic resonance imaging (MRI). We trained a deep U-Net machine learning model to delineate spinal canals on axial slices of 100 normal lumbar MRI scans which were previously delineated by expert radiologists and neurosurgeons. We then tested the model against lumbar MRI scans for 140 patients who had undergone lumbar spine MRI at our institution (60 of whom ultimately underwent surgery, and 80 of whom did not). The model generated automated segmentations of the lumbar spinal canals and calculated a maximum degree of spinal stenosis for each patient, which served as our biomarker for surgical pathology warranting expert consultation. The machine learning model correctly predicted surgical candidacy (ie, whether patients ultimately underwent lumbar spinal decompression) with high accuracy (area under the curve=0.88), using only imaging data from lumbar MRI scans. Automated interpretation of lumbar MRI scans was sufficient to correctly determine surgical candidacy in nearly 90% of cases. Given that a significant proportion of referrals placed for spine surgery evaluation fail to meet criteria for surgical intervention, our model could serve as a valuable tool for patient triage and thereby address some of the inefficiencies within the outpatient surgical referral process. Automated interpretation of lumbar MRI scans was sufficient to correctly determine surgical candidacy in nearly 90% of cases. CCS-1477 Given that a significant proportion of referrals placed for spine surgery evaluation fail to meet criteria for surgical intervention, our model could serve as a valuable tool for patient triage and thereby address some of the inefficiencies within the outpatient surgical referral process. An investigation for the causality of the effects of physical activity and specific sedentary activities on kidney function in the general population is warranted. In this observational cohort study, first, the clinical associations of the prevalence of stages 3-5 chronic kidney disease (CKD) and the eGFR with physical activity, determined by self-report or objective wrist-band accelerometer results, and sedentary activities (watching television, using a computer, and driving) were investigated in 329,758 UK Biobank participants. To assess causality, a two-sample Mendelian randomization (MR) analysis was performed to investigate the associations of a genetic predisposition to physical activity and a sedentary lifestyle with the risk of kidney function impairment in an independent CKDGen genome-wide association study (N = 567,460). The findings were replicated with the 321,024 UK white British Biobank participants in the allele-score-based one-sample MR. A higher degree of self-reported or accelerometer-determined moderate-to-vigorous physical activity was associated with a higher eGFR, while a longer time spent watching television was significantly associated with a lower eGFR and a higher prevalence of CKD. The two-sample MR demonstrated that the genetic predisposition to a higher degree of physical activity was associated with a lower risk of CKD and a higher eGFR, while the genetically predicted television watching duration was associated with a higher risk of CKD and a lower eGFR. The other sedentary behaviors yielded inconsistent results. The findings were similarly replicated in the one-sample MR. Physical activity and television watching causally affect kidney function in the general population. Physical activity and television watching causally affect kidney function in the general population.Molybdenum (Mo) is an essential element