analysing the advantages of the introduction and diversification of pedagogical approaches in Anatomy Education.Results: Anatomy Education's status quo is characterized by: less available teaching time, increasing demands from radiology and endoscopy imaging and other invasive and non-invasive medical techniques, increasing number of medical students and other logistical restrains exposed by the current Medical Education scenario. The traditional learning approach, mainly based on cadaveric dissection, is drifting to complementary newer technologies -such as 3D models or 2D/3D digital imaging -to examine the anatomy of the human body. Also, knowledge transfer is taking different channels, as learning management systems, social networks and computer-assisted learning and assessment are assuming relevant roles. Discussion: The future holds promising approaches for education models. The development of Artificial Intelligence, Virtual Reality and Learning Analytics could provide analytic tools towards a real-time and personalized learning process. Conclusion: A reflection on Anatomy Education, as a comprehensive model, allows us to understand Medical Education's complexity.Therefore, the present Medical Education context favours a blended learning approach, in which multi-modality pedagogical strategies may become the landmark.

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Purpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT).Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (<cutoff of normality), moderate (cutoff of normality ≤ pulmonary involvement < Z score 3), and severe pulmonary involvement (Z score ≥3).Results: Cohen's kappa agreement between CAD and radiologist classification was 81% (79–84%, 95% CI). The ROC curve of PI by the ANN presented a threshold of 21.5%, sensitivity of 0.80, specificity of 0.86, AUC of 0.90, accuracy of 0.82, F score of 0.85, and 0.65 Matthews' correlation coefficient. Accordingly, 77 patients were classified as having severe pulmonary involvement reaching 55 ± 13% of the TLV (Z score related to controls ≥3) and presented significantly higher lung weight, serum C-reactive protein concentration, proportion of hospitalization in intensive care units, instances of mechanical ventilation, and case fatality.Conclusion: The proposed CAD aided in detecting and quantifying the extent of pulmonary involvement, helping to phenotype patients with COVID-19 pneumonia.

Currently, medical education context poses different challenges to anatomy, contributing to the introduction of new pedagogical approaches, such as computer-assisted learning (CAL). This approach provides insight into students' learning profiles and skills that enhance anatomy knowledge acquisition. To understand the influence of anatomy CAL on spatial abilities, a study was conducted. A total of 671 medical students attending Musculoskeletal (MA) and Cardiovascular Anatomy (CA) courses, were allocated to one of three groups (MA Group, CA Group, MA + CA Group). Students' pre-training and post-training spatial abilities were assessed through Mental Rotations Test (MRT), with scores ranging between 0-24. After CAL training sessions, students' spatial abilities performance improved (9.72 ± 4.79 vs. 17.05 ± 4.57, P < 0.001). Although male students in both MA Group and CA Group show better baseline spatial abilities, no sex differences were found after CAL training. The improvement in spatial abilities score between sessions (Delta MRT) was correlated with Musculoskeletal Anatomy training sessions in MA Group (r = 0.333, P < 0.001) and MA + CA Group (r = 0.342, P < 0.001), and with Cardiovascular Anatomy training sessions in CA Group (r = 0.461, P = 0.001) and MA + CA Group (r = 0.324, P = 0.001). Multiple linear regression models were used, considering the Delta MRT as dependent variable. An association of Delta MRT to the amount of CAL training and the baseline spatial abilities was observed. The results suggest that CAL training in anatomy has positive dose-dependent effect on spatial abilities. Anat Sci Educ 00: 000-000. © 2018 American Association of Anatomists.