Mandibular Fracture Patterns in a Rural Setup: A 7-Year Retrospective Study.

Aim: This study is designed to outline the probable patterns of mandibular fracture based on patient demographics and mechanism of injury in a rural setup. Materials and Methods: The data from the record section in our unit belonging to patients who sustained fractures in the maxillofacial skeleton and were treated in our unit between the period June 2012-May 2019 were collected and analyzed. The variables analyzed for the study were etiology, gender, age, and type of fracture. All cases were treated by open reduction and rigid internal fixation. Results: A total of 224 patients with maxillofacial fractures were diagnosed, of which 195 were male and 29 were female. The ages ranged from 7 to 70 years. Road traffic accidents are noted to be the most common cause of mandibular fractures. The maximum cases were in the age group of 21-30 years with 85 (38%) patients. In a total of 224 patients, there were 278 mandibular fractures. The maximum incidence of fractures was in the mandibular parasymphysis region with 90 fractures accounting for 32.3% of the mandibular fractures. Males were more susceptible to mandibular fractures. Majority of them sustained mandibular fracture at more than one anatomical area. Conclusion: It can be concluded that mandibular fractures are seen predominantly in the second and third decades of life due to road traffic accidents with high-speed vehicles and lack of protective safety accessories. Mandible when it fractured, it usually involved more than one anatomical location.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.