Acquisition of endoscopic nasal surgery skills with a lamb's head model

Abstract Objectives: To evaluate the acquisition of surgical skills by otolaryngology residents and established the minimum number of dissections of a lamb's model to be performed before practicing on human patients. Methods: Nineteen second-year otolaryngology residents performed ten dissections each, five on each nasal cavity, always practicing the same three surgical procedures on the lamb model. Each student's training lasted 2-months, and the entire training intervention lasted 4-years, over four generations of residents. All dissections were recorded and were selected at random for examination by two independent otolaryngology surgeons, who were otherwise not involved in the research. Assessment of the 190 dissections used an instrument validated for surgical training of medical residents. Results: To a 1% significance level, statistical analysis revealed increased performance and satisfactory results were observed after the sixth dissection. Furthermore, after the eighth dissection, skill acquisition was relevant and sustained. Conclusion: Training in endoscopic nasal surgery on a lamb's head model improves surgical skills and handling of surgical instruments. Our results showed the relevance of the lamb model for training in otolaryngology surgery, impacting on patient safety. Level of evidence: 2.

Acquisition of endoscopic nasal surgery skills with a lamb's head model.

OBJECTIVES: To evaluate the acquisition of surgical skills by otolaryngology residents and established the minimum number of dissections of a lamb's model to be performed before practicing on human patients. METHODS: Nineteen second-year otolaryngology residents performed ten dissections each, five on each nasal cavity, always practicing the same three surgical procedures on the lamb model. Each student's training lasted 2-months, and the entire training intervention lasted 4-years, over four generations of residents. All dissections were recorded and were selected at random for examination by two independent otolaryngology surgeons, who were otherwise not involved in the research. Assessment of the 190 dissections used an instrument validated for surgical training of medical residents. RESULTS: To a 1% significance level, statistical analysis revealed increased performance and satisfactory results were observed after the sixth dissection. Furthermore, after the eighth dissection, skill acquisition was relevant and sustained. CONCLUSION: Training in endoscopic nasal surgery on a lamb's head model improves surgical skills and handling of surgical instruments. Our results showed the relevance of the lamb model for training in otolaryngology surgery, impacting on patient safety.

Estimating sex and age from a face: a forensic approach using machine learning based on photo-anthropometric indexes of the Brazilian population.

The facial analysis permits many investigations, some of the most important of which are craniofacial identification, facial recognition, and age and sex estimation. In forensics, photo-anthropometry describes the study of facial growth and allows the identification of patterns in facial skull development, for example, by using a group of cephalometric landmarks to estimate anthropological information. Previous works presented, as indirect applications, the use of photo-anthropometric measurements to estimate anthropological information such as age and sex. In several areas, automation of manual procedures has achieved advantages over and similar measurement confidence as a forensic expert. This manuscript presents an approach using photo-anthropometric indexes, generated from frontal faces cephalometric landmarks of the Brazilian population, to create an artificial neural network classifier that allows the estimation of anthropological information, in this specific case age and sex. This work is focused on four tasks: (i) sex estimation on ages from 5 to 22 years old, evaluating the interference of age on sex estimation; (ii) age estimation from photo-anthropometric indexes for four age intervals (1 year, 2 years, 4 years, and 5 years); (iii) age group estimation for thresholds of over 14 and over 18 years old; and; (iv) the provision of a new data set, available for academic purposes only, with a large and complete set of facial photo-anthropometric points marked and checked by forensic experts, measured from over 18,000 faces of individuals from Brazil over the last 4 years. The proposed binary classifier obtained significant results, using this new data set, for the sex estimation of individuals over 14 years old, achieving accuracy values higher than 0.85 by the F1 measure. For age estimation, the accuracy results are 0.72 for the F1 measure with an age interval of 5 years. For the age group estimation, the F1 measures of accuracy are higher than 0.93 and 0.83 for thresholds of 14 and 18 years, respectively.

Encoder-decoder optimization for brain-computer interfaces.

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.