Osteoporosis is characterized by bony material loss and decreased bone strength leading to a significant increase in fracture risk. Patient-specific quantitative computed tomography (QCT) finite element (FE) models may be used to predict fracture under physiological loading. Material properties for the FE models used to predict fracture are obtained by converting grayscale values from the CT into volumetric bone mineral density (vBMD) using calibration phantoms. If there are any variations arising from the CT acquisition protocol, vBMD estimation and material property assignment could be affected, thus, affecting fracture risk prediction. We hypothesized that material property assignments may be dependent on scanning and postprocessing settings including voltage, current, and reconstruction kernel, thus potentially having an effect in fracture risk prediction. A rabbit femur and a standard calibration phantom were imaged by QCT using different protocols. Cortical and cancellous regions were segmented, their average Hounsfield unit (HU) values obtained and converted to vBMD. Estimated vBMD for the cortical and cancellous regions were affected by voltage and kernel but not by current. Our study demonstrated that there exists a significant variation in the estimated vBMD values obtained with different scanning acquisitions. In addition, the large noise differences observed utilizing different scanning parameters could have an important negative effect on small subregions containing fewer voxels.

Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. “Ground truth” classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7–99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing–Bablok slope = 0.997 (95% CI: 0.988–0.999) and intercept = 0.001 (95% CI: 0.000–0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice.

The spine is the most common site for secondary bone metastases, and clinical management for fractures is based on size and geometry of the defect. About 75% of the bone needs to be damaged before lesions are detectable, so clinical tools should measure changes in both geometry and material properties. We have developed an automated, user-friendly, Spine Cancer Assessment (SCA) image-based analysis method that builds on a platform designed for clinical practice providing failure characteristics of vertebrae. The objectives of this study were to (1) validate SCA predictions with experimental failure load outcomes; (2) evaluate the planning capabilities for prophylactic vertebroplasty procedures; and (3) investigate the effect of computed tomography (CT) protocols on predicted failure loads. Twenty-one vertebrae were randomly divided into two groups: (1) simulated defect without treatment (negative control) [ n = 9] and (2) with treatment [ n = 12]. Defects were created and a polymeric biomaterial was injected into the vertebrae in the treated-defect group. Spines were scanned, reconstructed with two algorithms, and analyzed for fracture loads. To virtually plan for prophylactic intervention, vertebrae with empty lesions were simulated to be augmented with either poly(methyl methacrylate) (PMMA) or a novel bone replacement copolymer, poly(propylene fumarate-co-caprolactone) [P(PF-co-CL)]. Axial rigidities were calculated from the CT images. Failure loads, determined from the cross section with the lowest axial rigidity, were compared with experimental values. Predicted loads correlated well with experimental outcomes ( R ² = 0.73, p < 0.0001). Predictions from negative control specimens highly correlated with measured values ( R ² = 0.90, p < 0.0001). Although a similar correlation was obtained using both algorithms, the smooth reconstruction (B30) tended to underestimate predicted failure loads by ∼50% compared with the ∼10% underestimate of the sharp reconstruction (B70). Percent increase in failure loads after virtual vertebroplasty showed a higher increase in samples with PMMA compared with those with copolymer. The SCA method developed in this study calculated failure loads from quantitative computed tomography scans in vertebrae with simulated metastatic lytic defects, with or without treatment, facilitating clinical applicability and providing more reliable guidelines for physicians to select appropriate treatment options. Furthermore, the analysis could accommodate augmentation planning procedures that aimed to determine the optimum material that would increase vertebral body failure load.

Patients with chronic calorie insufficiency commonly suffer from upper gastrointestinal dysfunction and consequent dyspeptic symptoms, which may interfere with their nutritional rehabilitation. To investigate the relationship between gastric dysfunction and feeding behavior, we exposed mice to chronic caloric restriction and demonstrated gastric motor abnormalities in them. Gastric dysmotility is typically associated with dyspeptic symptoms but sensations cannot be directly assessed in animal models. Therefore, as an initial step toward establishing measurable correlates of postprandial symptoms in small animals, we have attempted to characterize central responses to food intake by positron emission tomography-computerized microtomography (PET-CT) in normal and calorically restricted mice. Animals consumed a standard test meal after an overnight fast before receiving 2-deoxy-2[¹⁸F]fluoro-D-glucose tracer. The same mice were also scanned in the fasting state on a separate day. We were able to bring the fed and fasting PET volume images into spatial registration with each other and with an MR-derived atlas of the mouse brain, so that the differences in uptake between the two states could be mapped quantitatively against the neuroanatomic regions of the atlas. Our approach is suitable for studying the effects of gastric dysmotilities on central responses to feeding.

Normal function of the gastrointestinal tract involves the coordinated activity of several cell types Human disorders of motor function of the gastrointestinal tract are often associated with changes in the number of these cells. For example, in diabetic patients, abnormalities in gastrointestinal transit are associated with changes in nerves and interstitial cells of Cajal (ICC), two key cells that generate and regulate motility. ICC are cells of mesenchymal origin that function as pacemakers and amplify neuronal signals in the gastrointestinal tract. Quantifying the changes in number of specific cell types in tissues from patients with motility disorders is challenging and requires immunolabeling for specific antigens. The shape of nuclei differs between the cell types in the wall of the gastrointestinal tract. Therefore the objective of this study was to determine whether cell nuclei can be classified by analyzing the 3D morphology of the nuclei. Furthermore, the orientation of the long axis of nuclei changes within and between the muscle layers. These features can be used to classify and differentially label the nuclei in confocal volume images of the tissue by computing the principal axis of the coordinates of the set of voxels forming each nucleus and thereby to identify cells by their nuclear morphology. Using this approach, we were able to separate and quantify nuclei in the smooth muscle layers of the tissue. Therefore we conclude that computer-aided classification of cell nuclei can be used to identify changes in the cell types expressed in gastrointestinal smooth muscle.