Expert-level Performance Research Articles

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

BackgroundChest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis.MethodsAn integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation.Results and conclusionsThe classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders

Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals. The area under the curves for malignancy detection were 0.928 ± 0.002 (Edinburgh) and 0.937 ± 0.004 (SNU). The area under the curves of primary treatment suggestion (SNU) were 0.828 ± 0.012, 0.885 ± 0.006, 0.885 ± 0.006, and 0.918 ± 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. For multi-class classification, the mean top-1 and top-5 accuracies were 56.7 ± 1.6% and 92.0 ± 1.1% (Edinburgh) and 44.8 ± 1.2% and 78.1 ± 0.3% (SNU), respectively. With the assistance of our algorithm, the sensitivity and specificity of 47 clinicians (21 dermatologists and 26 dermatology residents) for malignancy prediction (SNU; 240 images) were improved by 12.1% (P < 0.0001) and 1.1% (P < 0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P < 0.0001). The top-1 and top-3 accuracies of four doctors in the multi-class classification of 134 diseases (SNU; 2,201 images) were increased by 7.0% (P= 0.045) and 10.1% (P= 0.0020), respectively. The results suggest that our algorithm may serve as augmented intelligence that can empower medical professionals in diagnostic dermatology.

Train early and with deliberate practice: simple coronary surgery simulation platform results in fast increase in technical surgical skills in residents and students.

The amount of intense and focused training with the specific goal to improve performance (i.e. deliberate practice) is a predictor of expert-level performance in multiple domains of psychomotor skill learning. Simulation training improves surgical skills in cardiac surgery. We established a training programme that enables early surgical exposure and assessment. We investigated the training effects in coronary surgery simulations in trainees with different levels of surgical experience. The early surgical exposure and assessment programme comprises a low- and high-fidelity simulation, self-organized training, instructed workshops and a stepwise challenge increase. Performance was assessed with a multidimensional skill matrix using video recordings. Two groups of trainees [students (N = 7), 1-/2-year residents (N = 6)] completed introductory training (pretraining, level 1) and two 3-week training periods (levels 2 and 3). Fellows (N = 6) served as controls. Residents and students underwent deliberate practice training with specific training targets. Fellows performed regularly scheduled coronary surgery cases. Entry and exit assessments were conducted for levels 2 and 3. Fellows did not improve overall performance. Residents and students showed significant improvements in both technical accuracy and completion times. Residents reached an overall performance level comparable to fellows. Students reached similar accuracy of surgical skills with longer completion times [level 3 exit score/time: fellows 27 (24-29)/min; residents 27 (21-30)/min, P = 0.94; students 17 (17-25)/min, P = 0.068]. Deliberate practice training resulted in a fast and substantial increase in surgical skills in residents and students. Unexperienced residents reach performance levels of fellows. Deliberate practice simulation programmes should be a mandatory component of surgical training.

Practicing on the Advanced Training in Laparoscopic Suturing Curriculum (ATLAS): Is Mastery Learning in Residency Feasible to Achieve Expert-Level Performance in Laparoscopic Suturing?

Mastery learning assumes that given enough time and appropriate instructional strategies, most trainees will be able to achieve proficiency. Expert-level performance requires numerous hours of intensive and focus practice. We aimed to study whether it was possible for surgical trainees to achieve expert-derived proficiency level in laparoscopic suturing using the Advanced Training in Laparoscopic Suturing (ATLAS) curriculum over a short period of time. A multicenter IRB approved prospective study included surgery residents and minimally invasive fellows. Participants underwent weekly supervised instruction and assessments of ATLAS skills and self-directed practice between sessions over 12 weeks. Participants were asked to practice until they achieved previously established proficiency benchmarks of expert laparoscopic surgeons. Fifteen participants, PGY2 to PGY6, from 3 institutions practiced on the ATLAS curriculum. Three participants were able to achieve proficiency on the entire curriculum, with a cumulative practice time varying between 3.4 and 7.6 hours. Individual tasks had varying degrees of difficulty ranging from 85% proficiency on task 1 to 33.3% proficiency for task 6. Using a mixed-method model, the mean cumulative hours of practice to reach the benchmark threshold was estimated for each task and varied from 4.5 to 13.2 hours. The improved performance was associated with higher PGY level and proficiency in FLS. This study demonstrates that it is possible for some senior surgical trainees to achieve proficiency in an expert-level laparoscopic suturing curriculum. This study establishes a learning curve for each ATLAS individual task. Some learners may not be able to achieve proficiency on the entire curriculum over a short period of practice. Additional studies are needed to assess how to shorten the learning curve with effective instructional methods such as expert-guided training with immediate feedback.

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning.

To investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation. Hip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics. The binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification. A deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

Assessment of Expert-Level Automated Detection of Plasmodium falciparum in Digitized Thin Blood Smear Images

Decades of effort have been devoted to establishing an automated microscopic diagnosis of malaria, but there are challenges in achieving expert-level performance in real-world clinical settings because publicly available annotated data for benchmark and validation are required. To assess an expert-level malaria detection algorithm using a publicly available benchmark image data set. In this diagnostic study, clinically validated malaria image data sets, the Taiwan Images for Malaria Eradication (TIME), were created by digitizing thin blood smears acquired from patients with malaria selected from the biobank of the Taiwan Centers for Disease Control from January 1, 2003, to December 31, 2018. These smear images were annotated by 4 clinical laboratory scientists who worked in medical centers in Taiwan and trained for malaria microscopic diagnosis at the national reference laboratory of the Taiwan Centers for Disease Control. With TIME, a convolutional neural network-based object detection algorithm was developed for identification of malaria-infected red blood cells. A diagnostic challenge using another independent data set within TIME was performed to compare the algorithm performance against that of human experts as clinical validation. Performance on detecting Plasmodium falciparum-infected blood cells was measured by average precision, and performance on detecting P falciparum infection at the image level was measured using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The TIME data sets contained 8145 images of 36 blood smears from patients with suspected malaria (30 P falciparum-positive and 6 P falciparum-negative smears) that had reliable annotations. For clinical validation, the average precision was 0.885 for detecting P falciparum-infected blood cells and 0.838 for ring form. For detecting P falciparum infection on blood smear images, the algorithm had expert-level performance (sensitivity, 0.995; specificity, 0.900; AUC, 0.997 [95% CI, 0.993-0.999]), especially in detecting ring form (sensitivity, 0.968; specificity, 0.960; AUC, 0.995 [95% CI, 0.990-0.998]) compared with experienced microscopists (mean sensitivity, 0.995 [95% CI, 0.993-0.998]; mean specificity, 0.955 [95% CI, 0.885-1.000]). The findings suggest that a clinically validated expert-level malaria detection algorithm can be developed by using reliable data sets.

Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations.

The microscopic review of hematoxylin-eosin-stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex remains challenging. Both entities are distinct subtypes of human malformations of cortical development that share histopathological features consisting of neuronal dyslamination with dysmorphic neurons and balloon cells. We trained a convolutional neural network (CNN) to classify both entities and visualize the results. Additionally, we propose a new Web-based deep learning application as proof of concept of how deep learning could enter the pathologic routine. A digital processing pipeline was developed for a series of 56 cases of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex to obtain 4000 regions of interest and 200 000 subsamples with different zoom and rotation angles to train a neural network. Guided gradient-weighted class activation maps (Guided Grad-CAMs) were generated to visualize morphological features used by the CNN to distinguish both entities. Our best-performing network achieved 91% accuracy and 0.88 area under the receiver operating characteristic curve at the tile level for an unseen test set. Novel histopathologic patterns were found through the visualized Guided Grad-CAMs. These patterns were assembled into a classification score to augment decision-making in routine histopathology workup. This score was successfully validated by 11 expert neuropathologists and 12 nonexperts, boosting nonexperts to expert level performance. Our newly developed Web application combines the visualization of whole slide images with the possibility of deep learning-aided classification between focal cortical dysplasia IIb and tuberous sclerosis complex. This approach will help to introduce deep learning applications and visualization for the histopathologic diagnosis of rare and difficult-to-classify brain lesions.

Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images-Role of Multiscale Decision Aggregation and Data Augmentation.

Visual inspection of histopathology images of stained biopsy tissue by expert pathologists is the standard method for grading of prostate cancer (PCa). However, this process is time-consuming and subject to high inter-observer variability. Machine learning-based methods have the potential to improve efficient throughput of large volumes of slides while decreasing variability, but they are not easy to develop because they require substantial amounts of labeled training data. In this paper, we propose a deep learning-based classification technique and data augmentation methods for accurate grading of PCa in histopathology images in the presence of limited data. Our method combines the predictions of three separate convolutional neural networks (CNNs) that work with different patch sizes. This enables our method to take advantage of the greater amount of contextual information in larger patches as well as greater quantity of smaller patches in the labeled training data. The predictions produced by the three CNNs are combined using a logistic regression model, which is trained separately after the CNN training. To effectively train our models, we propose new data augmentation methods and empirically study their effects on the classification accuracy. The proposed method achieves an accuracy of [Formula: see text] in classifying cancerous patches versus benign patches and an accuracy of [Formula: see text] in classifying low-grade (i.e., Gleason grade 3) from high-grade (i.e., Gleason grades 4 and 5) patches. The agreement level of our automatic grading method with expert pathologists is within the range of agreement between pathologists. Our experiments indicate that data augmentation is necessary for achieving expert-level performance with deep learning-based methods. A combination of image-space augmentation and feature-space augmentation leads to the best results. Our study shows that well-designed and properly trained deep learning models can achieve PCa Gleason grading accuracy that is comparable to an expert pathologist.

Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation.

Biomedical image segmentation is lately dominated by deep neural networks (DNNs) due to their surpassing expert-level performance. However, the existing DNN models for biomedical image segmentation are generally highly parameterized, which severely impede their deployment on real-time platforms and portable devices. To tackle this difficulty, we propose an evolutionary compression method (ECDNN) to automatically discover efficient DNN architectures for biomedical image segmentation. Different from the existing studies, ECDNN can optimize network loss and number of parameters simultaneously during the evolution, and search for a set of Pareto-optimal solutions in a single run, which is useful for quantifying the tradeoff in satisfying different objectives, and flexible for compressing DNN when preference information is uncertain. In particular, a set of novel genetic operators is proposed for automatically identifying less important filters over the whole network. Moreover, a pruning operator is designed for eliminating convolutional filters from layers involved in feature map concatenation, which is commonly adopted in DNN architectures for capturing multi-level features from biomedical images. Experiments carried out on compressing DNN for retinal vessel and neuronal membrane segmentation tasks show that ECDNN can not only improve the performance without any retraining but also discover efficient network architectures that well maintain the performance. The superiority of the proposed method is further validated by comparison with the state-of-the-art methods.

Current applications and future directions of deep learning in musculoskeletal radiology.

Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of artificial intelligence that is ideally suited to solving image-based problems. There are an increasing number of musculoskeletal applications of deep learning, which can be conceptually divided into the categories of lesion detection, classification, segmentation, and non-interpretive tasks. Numerous examples of deep learning achieving expert-level performance in specific tasks in all four categories have been demonstrated in the past few years, although comprehensive interpretation of imaging examinations has not yet been achieved. It is important for the practicing musculoskeletal radiologist to understand the current scope of deep learning as it relates to musculoskeletal radiology. Interest in deep learning from researchers, radiology leadership, and industry continues to increase, and it is likely that these developments will impact the daily practice of musculoskeletal radiology in the near future.

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models.

Perfecting practice: a protocol for assessing simulation-based mastery learning and deliberate practice versus self-guided practice for bougie-assisted cricothyroidotomy performance

BackgroundSimulation-based medical education (SBME) is a cornerstone for procedural skill training in residency education. Multiple studies have concluded that SBME is highly effective, superior to traditional clinical education, and translates to improved patient outcomes. Additionally it is widely accepted that mastery learning, which comprises deliberate practice, is essential for expert level performance for routine skills; however, given that highly structured practice is more time and resource-intensive, it is important to assess its value for the acquisition of rarely performed technical skills. The bougie-assisted cricothyroidotomy (BAC), a rarely performed, lifesaving procedure, is an ideal skill for evaluating the utility of highly structured practice as it is relevant across many acute care specialties and rare – making it unlikely for learners to have had significant previous training or clinical experience. The purpose of this study is to compare a modified mastery learning approach with deliberate practice versus self-guided practice on technical skill performance using a bougie-assisted cricothyroidotomy model.MethodsA multi-centre, randomized study will be conducted at four Canadian and one American residency programs with 160 residents assigned to either mastery learning and deliberate practice (ML + DP), or self-guided practice for BAC. Skill performance, using a global rating scale, will be assessed before, immediately after practice, and 6 months later. The two groups will be compared to assess whether the type of practice impacts performance and skill retention.DiscussionMastery learning coupled with deliberate practice provides systematic and focused feedback during skill acquisition. However, it is resource-intensive and its efficacy is not fully defined. This multi-centre study will provide generalizable data about the utility of highly structured practice for technical skill acquisition of a rare, lifesaving procedure within postgraduate medical education. Study findings will guide educators in the selection of an optimal training strategy, addressing both short and long term performance.

Dilated-Inception Net: Multi-Scale Feature Aggregation for Cardiac Right Ventricle Segmentation.

Segmentation of cardiac ventricle from magnetic resonance images is significant for cardiac disease diagnosis, progression assessment, and monitoring cardiac conditions. Manual segmentation is so time consuming, tedious, and subjective that automated segmentation methods are highly desired in practice. However, conventional segmentation methods performed poorly in cardiac ventricle, especially in the right ventricle. Compared with the left ventricle, whose shape is a simple thick-walled circle, the structure of the right ventricle is more complex due to ambiguous boundary, irregular cavity, and variable crescent shape. Hence, effective feature extractors and segmentation models are preferred. In this paper, we propose a dilated-inception net (DIN) to extract and aggregate multi-scale features for right ventricle segmentation. The DIN outperforms many state-of-the-art models on the benchmark database of right ventricle segmentation challenge. In addition, the experimental results indicate that the proposed model has potential to reach expert-level performance in right ventricular epicardium segmentation. More importantly, DIN behaves similarly to clinical expert with high correlation coefficients in four clinical cardiac indices. Therefore, the proposed DIN is promising for automated cardiac right ventricle segmentation in clinical applications.

Expert-level sleep scoring with deep neural networks.

ObjectivesScoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data.MethodsWe used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs.ResultsWhen trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index.ConclusionsBy creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

BackgroundThere is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce.ResultsHere, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning.ConclusionRoutine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

A Comparison of Head-Hand Coordination Patterns during Squash Forehand Strokes in Expert and Less-Skilled Squash Players

Objective: To compare head and hand movement patterns during squash forehand motions between experts and less-skilled squash players. Method: Four experts and four less-skilled squash players participated in this study. They performed squash forehand swings and a VICON motion analysis system was used to obtain displacement and velocity data of the head and right hand during the movement. Mann-Whitney U-tests were performed to compare head and hand range of motion and peak velocity, and cross-correlation was performed to analyze the head-hand coordination pattern between groups in three movement directions. Results: In terms of head and hand kinematic data, experts had greater head range of motion during down swings than less-skilled squash players. Experts seemed to reach peak hand velocity at impact by reaching peak head velocity followed by hand peak velocity within a given temporal sequence. In terms of headhand coordination patterns, both groups revealed high positive correlations in the medial-lateral direction, indicating a dominant allocentric coordination pattern. However, experts had uncoupled coordination patterns in the vertical direction and less-skilled squash players had high positive correlations. These results indicate that the head-hand movement pattern likely an important factor squash forehand movement. Conclusion: Analysis of head and hand movement patterns could be a key variable in squash training to reach expert-level performance.

Expert youth coaches’ diversification strategies in talent development: A qualitative typology

The development of expert-level performance in sports is discussed against the background of two different pathways. The early specialization approach emphasizes both early onset and high volumes of sport-specific practice in a desired main sport. On the other hand, the diversification approach promotes diversified involvement in a range of other sports with later specialization. This study examines the way in which youth coaches translate the antagonistic concepts of specialization and diversification into their coaching strategies during the early stages of talent development. Using qualitative research methodology, 44 expert German youth coaches (M age = 45.1 years, SD = 7.5; 39 male and 5 female) in 24 different sports, with an average of 21.7 years accumulated coaching experience (SD = 7.0) were included in the inductive thematic analysis. Analysis showed a differentiated understanding of the process of specialization that considered multiple ways to apply diversification both within sports and across several sports. Although all of the coaches appeared to acknowledge the importance of within-sports diversification, there was considerable variation in the reported purposeful implementation and significance of sporting activities other than the main sport leading to a nuanced typology of strategies. The typology could be divided into the following four categories based on the preferred strategies that the coaches described: (I) changers and late entrants; (II) early engagement in DS + secondary sports; (III) early engagement in DS + supplementary sports; and (IV) early engagement in DS + specialization. The findings strengthened existing suggestions of a gradual and multidimensional understanding of diversification prior to necessary specialization. Furthermore, it can be assumed that the preferred strategies are fundamentally influenced by individual cognitions and contextual aspects that acknowledge the complex and ideographical nature of coaching.

Tracking Plasticity: Effects of Long-Term Rehearsal in Expert Dancers Encoding Music to Movement.

Our knowledge of neural plasticity suggests that neural networks show adaptation to environmental and intrinsic change. In particular, studies investigating the neuroplastic changes associated with learning and practicing motor tasks have shown that practicing such tasks results in an increase in neural activation in several specific brain regions. However, studies comparing experts and non-experts suggest that experts employ less neuronal activation than non-experts when performing a familiar motor task. Here, we aimed to determine the long-term changes in neural networks associated with learning a new dance in professional ballet dancers over 34 weeks. Subjects visualized dance movements to music while undergoing fMRI scanning at four time points over 34-weeks. Results demonstrated that initial learning and performance at seven weeks led to increases in activation in cortical regions during visualization compared to the first week. However, at 34 weeks, the cortical networks showed reduced activation compared to week seven. Specifically, motor learning and performance over the 34 weeks showed the typical inverted-U-shaped function of learning. Further, our result demonstrate that learning of a motor sequence of dance movements to music in the real world can be visualized by expert dancers using fMRI and capture highly significant modeled fits of the brain network variance of BOLD signals from early learning to expert level performance.

Using a blog as an integrated eLearning tool and platform

Technology enhanced learning or eLearning allows educators to expand access to educational content, promotes engagement with students and makes it easier for students to access educational material at a time, place and pace which suits them. The challenge for educators beginning their eLearning journey is to decide where to start, which includes the choice of an eLearning tool and platform. This article will share one educator’s decision making process, and experience using blogs as a flexible and versatile integrated eLearning tool and platform. Apart from being a cost effective/free tool and platform, blogs offer the possibility of creating a hyperlinked indexed content repository, for both created and curated educational material; as well as a distribution and engagement tool and platform. Incorporating pedagogically sound activities and educational practices into a blog promote a structured templated teaching process, which can be reproduced. Moving from undergraduate to postgraduate training, educational blogs supported by a comprehensive online case-based repository offer the possibility of training beyond competency towards proficiency and expert level performance through a process of deliberate practice. By documenting educational content and the student engagement and learning process, as well as feedback and personal reflection of educational sessions, blogs can also form the basis for a teaching portfolio, and provide evidence and data of scholarly teaching and educational scholarship. Looking into the future, having a collection of readily accessible indexed hyperlinked teaching material offers the potential to do on the spot teaching with illustrative material called up onto smart surfaces, and displayed on holographic interfaces.

Expert music performance: cognitive, neural, and developmental bases.

In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work.