Medical Modeling and Fabrication of Surgical Guides

Medical Modelling and Rapid Prototyping are being used extensively to produce accurate implants. Rapid Prototyping has been used in making 3D medical models. The models help in personalizing pre-surgical treatment and pre-operative planning. Medical modelling is used for creation of high precision physical models from medical scans. The process includes collecting scans. The process includes collecting human anatomy data and optimizing the data for manufacturing and creating models using rapid prototyping. Computer aided software is used for prototyping. To produce high precision models, the modelling has to be in 3D. Therefore, a proper scanning method has to be used for each type of surgery. In this work we have taken a case study of fingers of hand to design and develop the 3D model for each case. We have designed, developed and fabricated one of the surgical guides for the fingers of hand. The design and fabrication was done in the 3D slicer and Grab CAD software independently. This work provides the approach and systematic procedure for the design and fabrication of surgical guide.

Wearable Biosensors for Non-Invasive Sweat Diagnostics

Recent advances in microfluidics, microelectronics, and electrochemical sensing methods have steered the way for the development of novel and potential wearable biosensors for healthcare monitoring. Wearable bioelectronics has received tremendous attention worldwide due to its great a potential for predictive medical modeling and allowing for personalized point-of-care-testing (POCT). They possess many appealing characteristics, for example, lightweight, flexibility, good stretchability, conformability, and low cost. These characteristics make wearable bioelectronics a promising platform for personalized devices. In this paper, we review recent progress in flexible and wearable sensors for non-invasive biomonitoring using sweat as the bio-fluid. Real-time and molecular-level monitoring of personal health states can be achieved with sweat-based or perspiration-based wearable biosensors. The suitability of sweat and its potential in healthcare monitoring, sweat extraction, and the challenges encountered in sweat-based analysis are summarized. The paper also discusses challenges that still hinder the full-fledged development of sweat-based wearables and presents the areas of future research.

A comparison of attentional neural network architectures for modeling with electronic medical records

Objective Attention networks learn an intelligent weighted averaging mechanism over a series of entities, providing increases to both performance and interpretability. In this article, we propose a novel time-aware transformer-based network and compare it to another leading model with similar characteristics. We also decompose model performance along several critical axes and examine which features contribute most to our model’s performance. Materials and methods Using data sets representing patient records obtained between 2017 and 2019 by the Kaiser Permanente Mid-Atlantic States medical system, we construct four attentional models with varying levels of complexity on two targets (patient mortality and hospitalization). We examine how incorporating transfer learning and demographic features contribute to model success. We also test the performance of a model proposed in recent medical modeling literature. We compare these models with out-of-sample data using the area under the receiver-operator characteristic (AUROC) curve and average precision as measures of performance. We also analyze the attentional weights assigned by these models to patient diagnoses. Results We found that our model significantly outperformed the alternative on a mortality prediction task (91.96% AUROC against 73.82% AUROC). Our model also outperformed on the hospitalization task, although the models were significantly more competitive in that space (82.41% AUROC against 80.33% AUROC). Furthermore, we found that demographic features and transfer learning features which are frequently omitted from new models proposed in the EMR modeling space contributed significantly to the success of our model. Discussion We proposed an original construction of deep learning electronic medical record models which achieved very strong performance. We found that our unique model construction outperformed on several tasks in comparison to a leading literature alternative, even when input data was held constant between them. We obtained further improvements by incorporating several methods that are frequently overlooked in new model proposals, suggesting that it will be useful to explore these options further in the future.

The Role of Virtual Surgical Planning in Increasing the Predictability in Orthognathic Surgery

Purpose: To evaluate our surgical outcomes by comparing our surgical plan to the outcome of the surgery and evaluate our efficacy using Virtual Surgical Planning and Medical Modeling software. Our aim is to determine the quality and validity of Virtual Surgical Planning when comparing pre-surgical plans with post-surgical outcomes. Patients and Methods: A cohort study was conducted for patients who underwent orthognathic surgery at a single institution. Utilizing virtual plans and models, select points for the virtual plans were compared and superimposed with that of the actual surgical movements. The primary predictor variable were the pre-surgical virtual plans of movements; the outcome variable consisted of the actual post-surgical movements. Statistical analysis was computed via IBM SPSS Version 25 software utilizing a paired t-test assuming equal variance with alpha (p<0.05). The sample of patients included those who had pre-operative and post-operative cone beam computed tomography scans, a virtual surgical plan, CAD/CAM splints, and LeFort I osteotomy and bilateral sagittal split osteotomy.Results: The study consisted of ten patients between the ages of 18-51 years old. Pre and post surgical plans were superimposed and four points of measurement were compared along 3 dimensional planes. There were no statistical significant associations between the virtually planned and post surgical planned values.Conclusion: Our results suggest that the use of virtual surgical planning in orthognathic surgery yields favorable and accurate surgical outcomes regarding rotational movements with minor degrees of discrepancies.