Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users.

In this paper, we present a human-based computation approach for the analysis of peripheral blood smear (PBS) images images in patients with Sickle Cell Disease (SCD). We used the Mechanical Turk microtask market to crowdsource the labeling of PBS images. We then use the expert-tagged erythrocytesIDB dataset to assess the accuracy and reliability of our proposal. Our results showed that when a robust consensus is achieved among the Mechanical Turk workers, probability of error is very low, based on comparison with expert analysis. This suggests that our proposed approach can be used to annotate datasets of PBS images, which can then be used to train automated methods for the diagnosis of SCD. In future work, we plan to explore the potential integration of our findings with outcomes obtained through automated methodologies. This could lead to the development of more accurate and reliable methods for the diagnosis of SCD.

Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images.

In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cells. In our case we used samples of patients with sickle-cell disease which can be generalized for other study cases. To trust the behavior of the proposed system, we also analyzed the interpretability. We pre-processed and segmented microscopic images, to ensure high feature quality. We applied the methods used in the literature to extract the features from blood cells and the machine learning methods to classify their morphology. Next, we searched for their best parameters from the resulting data in the feature extraction phase. Then, we found the best parameters for every classifier using Randomized and Grid search. For the sake of scientific progress, we published parameters for each classifier, the implemented code library, the confusion matrices with the raw data, and we used the public erythrocytesIDB dataset for validation. We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models. Finally, comparing the best performing classification methods with the state-of-the-art, we obtained better results even with interpretable model classifiers.

Crowdsourcing human-based computation for medical image analysis: A systematic literature review.

Computer-assisted algorithms for the analysis of medical images require human interactions to achieve satisfying results. Human-based computation and crowdsourcing offer a solution to this problem. We performed a systematic literature review of studies on crowdsourcing human-based computation for medical image analysis based on the guidelines proposed by Kitchenham and Charters. We identified 43 studies relevant to the objective of this research. We determined three primary purposes and problems that crowdsourcing human-based computation systems can solve. We found that the users provided five information types. We compared systems that use pre-, post-evaluation and quality control methods to select and filter the user inputs. We analyzed the metrics used for the evaluation of the crowdsourcing human-based computation system performance. Finally, we identified the most popular crowdsourcing human-based computation platforms with their advantages and disadvantages.Crowdsourcing human-based computation systems can successfully solve medical image analysis problems. However, the application of crowdsourcing human-based computation systems in this research area is still limited and more studies should be conducted to obtain generalizable results. We provided guidelines to practitioners and researchers based on the results obtained in this research.

Vision-based serious games and virtual reality systems for motor rehabilitation: A review geared toward a research methodology.

BACKGROUND: Nowadays, information technologies are being widely adopted to promote healthcare and rehabilitation. Owing to their affordability and use of hand-free controllers, vision-based systems have gradually been integrated into motor rehabilitation programs and have greatly drawn the interest of healthcare practitioners and the research community. Many studies have illustrated the effectiveness of these systems in rehabilitation. However, the report and design aspects of the reported clinical trials were disregarded. OBJECTIVE: In this paper, we present a systematic literature review of the use of vision-based serious games and virtual reality systems in motor rehabilitation programs. We aim to propose a research methodology that engineers can use to improve the designing and reporting processes of their clinical trials. METHODS: We conducted a review of published studies that entail clinical experiments. Searches were performed using Web of Science and Medline (PubMed) electronic databases, and selected studies were assessed using the Downs and Black Checklist and then analyzed according to specific research questions. RESULTS: We identified 86 studies and our findings indicate that the number of studies in this field is increasing, with Korea and USA in the lead. We found that Kinect, EyeToy system, and GestureTek IREX are the most commonly used technologies in studying the effects of vision-based serious games and virtual reality systems on rehabilitation. Findings also suggest that cerebral palsy and stroke patients are the main target groups, with a particular interest on the elderly patients in this target population. The findings indicate that most of the studies focused on postural control and upper extremity exercises and used different measurements during assessment. CONCLUSIONS: Although the research community's interest in this area is growing, many clinical trials lack sufficient clarity in many aspects and are not standardized. Some recommendations have been made throughout the article.

Feasibility of Kinect-Based Games for Balance Rehabilitation: A Case Study.

We aimed at determining the effects of prototype games on older adults attending a rehabilitation program in an elderly house in this work. We conducted an initial case study where two participants underwent a 5-week intervention. Feasibility was assessed by examining recruitment, adherence, and safety. The Tinetti balance test was used as pretest and posttest assessments. Results show that adherence was very high and no adverse effects were registered during the sessions. The included participants also reported enjoyment during the playtime and exhibited improvements in Tinetti scores. The findings suggest that game-based rehabilitation can be useful for improving balance in elderly people and can be incorporated in a fall prevention program.