Classification of the level of mandibular atrophy - A computer-assisted study based on 500 CT scans.

OBJECTIVE: For the optimal treatment of patients with highly atrophic mandibles, it is required to assess and quantify the extent of atrophy. The classification schemes that are well established today are known to be limited with respect to objectivity and reproducibility. Thus, the aim of the study was to generate a computer-aided method of classification, investigate its applicability in comparison with the established methods, and apply it to a large set of data. MATERIALS AND METHODS: Mandibular geometries were segmented from 500 Multislice (MSCT) datasets of atrophic and non-atrophic mandibles and automatically processed to gain virtual images of the mandibular cross-sections. Three different human investigators classified these data according to Cawood and Howell's classification scheme. Additionally, a tailored computer algorithm was applied that could work automatically and thus be observer independent. Furthermore, geometrical properties of the mandibles were investigated, statistically analysed, and correlated to the protocolled dental status and to the human and computer-generated classifications. RESULTS: Whilst the atrophy classification scheme showed highly significant correlation to the local dimensions of the alveolar ridge, its reproducibility was limited. It was shown that the human classifiers could not objectively classify the mandibular atrophy according to the established methods, with only 60.9% of decisions being unequivocal. The computer-aided method showed similar results to the human investigators. CONCLUSION: It is feasible to develop computer-aided procedures for the objective and fully reproducible classification of the level of atrophy. With further research, the established classification scheme may be ameliorated with the aid of computational methods.

Innovative practice of project-based learning and 3 D printing in Computer-aided medicine / 中华医学图书情报杂志

After the application of project-based learning (PBL) and 3D printing in classroom and teaching, the integrating methods and principles of PBL and 3D printing and recent teaching resources were summarized, PBL-based 3D modeling combined with recent innovative practice of 3D printing teaching model, with the course of Computer-aided medicine as an example, showed that the new teaching mode can effectively stimulate the interests of students, and cultivate their innovative thinking.

Toward cognitive pipelines of medical assistance algorithms.

PURPOSE: Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain. METHODS: We propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings. RESULTS: Our results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time. CONCLUSION: The proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.