A virtual reality simulator for training the surgical reduction of patient-specific supracondylar humerus fractures.

PURPOSE: Virtual reality has been used as a training platform in medicine, allowing the repetition of a situation/scenario as many times as needed and making it patient-specific prior to an operation. Of special interest is the minimally invasive plate osteosynthesis (MIPO). It represents a novel technique for orthopedic trauma surgery, but requires intensive training to acquire the required skills. In this paper, we propose a virtual reality platform for training the surgical reduction of supracondylar fractures of the humerus using MIPO. The system presents a detailed surgical theater where the surgeon has to place the bone fragments properly. METHODS: Seven experienced users were selected to perform a surgical reduction using our proposal. Two paired humeri were scanned from a dataset obtained from the Complejo Hospitalario de Jaén. A virtual fracture was performed in one side of the pair, using the other as contralateral part. Users have to simulate a reduction for each case and fill out a survey about usability, using a five-option Likert scale. RESULTS: The subjects have obtained excellent scores in both simulations. The users have notably reduced the time employed in the second experiment, being 60% less in average. Subjects have valued the usability (5.0), the intuitiveness (4.6), comfort (4.5), and realism (4.9) in a 1-5 Likert scale. The mean score of the usability survey was 4.66. CONCLUSION: The system has shown a high learning rate, and it is expected that the trainees will reach an expert level after additional runs. By focusing on the movement of bone fragments, specialists acquire motor skills to avoid the malrotation of MIPO-treated fractures. A future study can fulfill the requirements needed to include this training system into the protocol of real surgeries. Therefore, we expect the system to increase the confidence of the trainees as well as to improve their decision making.

Hybrid curvature-geometrical detection of landmarks for the automatic analysis of the reduction of supracondylar fractures of the femur.

BACKGROUND AND OBJECTIVE: The analysis of the features of certain tissues is required by many procedures of modern medicine, allowing the development of more efficient treatments. The recognition of landmarks allows the planning of orthopedic and trauma surgical procedures, such as the design of prostheses or the treatment of fractures. Formerly, their detection has been carried out by hand, making the workflow inaccurate and tedious. In this paper we propose an automatic algorithm for the detection of landmarks of human femurs and an analysis of the quality of the reduction of supracondylar fractures. METHODS: The detection of anatomical landmarks follows a knowledge-based approach, consisting of a hybrid strategy: curvature and spatial decomposition. Prior training is unrequired. The analysis of the reduction quality is performed by a side-to-side comparison between healthy and fractured sides. The pre-clinical validation of the technique consists of a two-stage study: Initially, we tested our algorithm with 14 healthy femurs, comparing the output with ground truth values. Then, a total of 140 virtual fractures was processed to assess the validity of our analysis of the quality of reduction. A two-sample t test and correlation coefficients between metrics and the degree of reduction have been employed to determine the reliability of the algorithm. RESULTS: The average detection error of landmarks was maintained below 1.7 mm and 2∘ (p< 0.01) for points and axes, respectively. Regarding the contralateral analysis, the resulting P-values reveal the possibility to determine whether a supracondylar fracture is properly reduced or not with a 95% of confidence. Furthermore, the correlation is high between the metrics and the quality of the reduction. CONCLUSIONS: This research concludes that our technique allows to classify supracondylar fracture reductions of the femur by only analyzing the detected anatomical landmarks. A initial training set is not required as input of our algorithm.

The role of virtual and augmented reality in orthopedic trauma surgery: From diagnosis to rehabilitation.

Virtual and augmented reality have been used to assist and improve human capabilities in many fields. Most recent advances allow the usage of these technologies for personal and professional purposes. In particular, they have been progressively introduced in many medical procedures since the last century. Thanks to immersive training systems and a better comprehension of the ongoing procedure, their main objectives are to increase patient safety and decrease recovery time. The current and future possibilities of virtual and augmented reality in the context of bone fracture reduction are the main focus of this review. This medical procedure requires meticulous planning and a complex intervention in many cases, hence becoming a promising candidate to be benefited from this kind of technology. In this paper, we exhaustively analyze the impact of virtual and augmented reality to bone fracture healing, detailing each task from diagnosis to rehabilitation. Our primary goal is to introduce novel researchers to current trends applied to orthopedic trauma surgery, proposing new lines of research. To that end, we propose and evaluate a set of qualitative metrics to highlight the most promising challenges of virtual and augmented reality technologies in this context.

Automatic detection of landmarks for the analysis of a reduction of supracondylar fractures of the humerus.

An accurate identification of bone features is required by modern orthopedics to improve patient recovery. The analysis of landmarks enables the planning of a fracture reduction surgery, designing prostheses or fixation devices, and showing deformities accurately. The recognition of these features was previously performed manually. However, this long and tedious process provided insufficient accuracy. In this paper, we propose a geometrically-based algorithm that automatically detects the most significant landmarks of a humerus. By employing contralateral images of the upper limb, a side-to-side study of the landmarks is also conducted to analyze the goodness of supracondylar fracture reductions. We conclude that a reduction can be classified by only considering the detected landmarks. In addition, our technique does not require a prior training, thus becoming a reliable alternative to treat this kind of fractures.