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  1. 5781
    “…All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. …”
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    Online Artículo Texto
    Personalized hypertension treatment recommendations by a data-driven model
  2. 5782
    “…RESULTS: The synthetic μ-map demonstrated a strong correlation (R(2) = 0.972) and minimum error (mean square error = 0.936 × 10(−4), %normalized mean absolute error = 0.999%) of attenuation coefficients when compared to the ground truth (n = 30). Compared to manual segmentation, the automatic thyroid segmentation was excellent with a Dice similarity coefficient of 0.767, minimal thyroid volume difference of − 0.72 mL, and a short 95% Hausdorff distance of 9.416 mm (n = 36). …”
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    CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning
  3. 5783
    “…CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.…”
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    Online Artículo Texto
    Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models
  4. 5784
    “…Recovery coefficients (RCs) of four quantitative metrics, including standardized uptake value (SUV)(max), SUV(ISO-50) (SUV(mean) in 50% isocontour), SUV(peak), and mean uptake of 10 highest concentration voxels were evaluated as RC(max), RC(ISO-50), RC(peak), and RC(10V), representing percent difference relative to the static ground truth case as functions of sphere sizes. A set of image reconstruction parameters was proposed for harmonized reconstruction to minimize variability between scanners. …”
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    Online Artículo Texto
    Multicenter quantitative (18)F-fluorodeoxyglucose positron emission tomography performance harmonization: use of hottest voxels towards more robust quantification
  5. 5785
    “…We were willing to share the truth of our lived experience. We were treated as co-researchers. …”
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    Online Artículo Texto
    Co-learning commentary: a patient partner perspective in mental health care research
  6. 5786
    “…Persons interpreting such clinically irrelevant data should do so with caution, as the results are unlikely to reflect the truth of what the questions intend to determine. …”
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    Meaningless Use: Assessing Compliance With a Clinically Meaningless Emergency Department Documentation Requirement
  7. 5787
    “…Consensus agreement between three experienced fellowship-trained trauma surgeons was defined as the ground truth label. Diagnostic accuracy and area under the receiver operator characteristic curve (AUC) were used to assess classification performance. …”
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    Online Artículo Texto
    Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN)
  8. 5788
    “…The validation scripts TruthTable.R, SensitivityAnalysis.R, and ScoreNet.R provide various readouts for the comparison of model predictions to experimental data. …”
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    Online Artículo Texto
    kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models
  9. 5789
    “…Using this data, we look across all the programs to ascertain which of these characteristics are important to the success of these six programs. Using a truth table, we then compare the programs which have more than 80% of the characteristics with those that have fewer than 80%, to identify characteristics that distinguish between them. …”
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    Online Artículo Texto
    Evolution of community health workers: the fourth stage
  10. 5790
    “…The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. …”
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    Online Artículo Texto
    Cardiovascular disease/stroke risk stratification in deep learning framework: a review
  11. 5791
    “…Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL–based vs. standard CAD were calculated. …”
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    Online Artículo Texto
    “Will I change nodule management recommendations if I change my CAD system?”—impact of volumetric deviation between different CAD systems on lesion management
  12. 5792
    “…Compared with the ground truth (QCT) images, the accuracy of the BMD measurements from the QCBCT images showed an RMSE of 83.41 mg/cm(3), MAE of 67.94 mg/cm(3), and MAPE of 8.32% across all the bone sites of interest, whereas for the CAL_CBCT images, those values were 491.15 mg/cm(3), 460.52 mg/cm(3), and 54.29%, respectively. …”
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    Online Artículo Texto
    Validation of bone mineral density measurement using quantitative CBCT image based on deep learning
  13. 5793
    “…The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). …”
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    Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN
  14. 5794
    “…Dice coefficient (DC) measured segmentation overlap, considering manual segmentation the ground truth. Lymphoma lesions were characterized by 31 features. …”
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    Online Artículo Texto
    Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [(18)F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization
  15. 5795
    por Zhai, Jinxia, Zhang, Haisheng
    Publicado 2023
    “…Among the three dimensions of seeking truth, open mind, and cognitive maturity, the scores of males were 38.26 ± 7.48, 38.78 ± 6.46 and 41.03 ± 8.69, which were lower than females’ 39.97 ± 7.11, 40.48 ± 6.48 and 44.91 ± 7.60, respectively. …”
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    Online Artículo Texto
    Critical thinking disposition of medical students in Anhui Province, China: a cross-sectional investigation
  16. 5796
    “…Representatively, in the healthy brain in‐silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground‐truth conductivity (2.00, 0.30, 0.50 S/m) than the integral‐based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). …”
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    Online Artículo Texto
    Data‐driven electrical conductivity brain imaging using 3 T MRI
  17. 5797
    “…A DNP was trained on manual segmentations of the putamen as ground truth. For this, the cohort was randomly split into a training (N = 131) and test set (N = 120). …”
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    Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy
  18. 5798
    “…The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. …”
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    Online Artículo Texto
    Deep learning-based diagnosis of disease activity in patients with Graves’ orbitopathy using orbital SPECT/CT
  19. 5799
    “…Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). …”
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    Online Artículo Texto
    MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network
  20. 5800
    “…METHODS: Craniomaxillofacial (CMF) CT data of 80 patients with dentomaxillofacial deformities were collected for model development. 77 anatomical landmarks digitized by experienced CMF surgeons in each CT image were set as the ground truth. 3D UX-Net, the cutting-edge medical image segmentation network, was adopted as the backbone of model architecture. …”
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    Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model
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