A comparative study of visual assessment between dry bone, 2-dimensional photograph, and deep learning methods in sex classification on the auricular area of the os coxae in a Thai population / Un estudio comparativo de la evaluación visual entre el hueso seco, la fotografía bidimensional y los métodos de aprendizaje profundo en la clasificación del sexo en el área auricular del hueso coxal en una población tailandesa

SUMMARY: Sex assessment is an important process in forensic identification. A pelvis is the best skeletal element for identifying sexes due to its sexually dimorphic morphology. This study aimed to compare the accuracy of the visual assessment in dry bones as well as 2D images and to test the accuracy of using a deep convolutional neural network (GoogLeNet) for increasing the performance of a sex determination tool in a Thai population. The total samples consisted of 250 left os coxa that were divided into 200 as a 'training' group (100 females, 100 males) and 50 as a 'test' group. In this study, we observed the auricular area, both hands-on and photographically, for visual assessment and classified the images using GoogLeNet. The intra-inter observer reliabilities were tested for each visual assessment method. Additionally, the validation and test accuracies were 85, 72 percent and 79.5, 60 percent, for dry bone and 2D image methods, respectively. The intra- and inter-observer reliabilities showed moderate agreement (Kappa = 0.54 - 0.67) for both visual assessments. The deep convolutional neural network method showed high accuracy for both validation and test sets (93.33 percent and 88 percent, respectively). Deep learning performed better in classifying sexes from auricular area images than other visual assessment methods. This study suggests that deep learning has advantages in terms of sex classification in Thai samples.

RESUMEN: La evaluación del sexo es un proceso importante en la identificación forense. La pelvis es el mejor elemento esquelético para identificar sexos debido a su morfología sexualmente dimórfica. Este estudio tuvo como objetivo comparar la precisión de la evaluación visual en huesos secos, así como imágenes 2D y probar la precisión del uso de una red neuronal convolucional profunda (GoogLeNet) para aumentar el rendimiento de una herramienta de determinación de sexo en una población tailandesa. Las muestras consistieron en 250 huesos coxales izquierdos, los que fueron dividi- das de la siguiente manera: 200 como un grupo de "entrenamiento" (100 mujeres, 100 hombres) y 50 como un grupo de "prueba". En este estudio, observamos el área auricular, tanto de forma práctica como fotográfica, para una evaluación visual y clasificamos las imágenes utilizando GoogLeNet. Se analizó la confiabilidad intra-interobservador para cada método de evaluación visual. Además, las precisiones de validación y prueba fueron del 85, 72 por ciento y 79,5, 60 por ciento, para los métodos de hueso seco y de imágenes 2D, respectivamente. Las confiabilidades intra e interobservador mostraron un acuerdo moderado (Kappa = 0.54 - 0.67) para ambas evaluaciones visuales. El método de red neuronal convolucional profunda mostró una alta precisión tanto para la validación como para los conjuntos de prueba (93,33 por ciento y 88 por ciento, respectivamente). El aprendizaje se desempeñó mejor en la clasificación de sexos a partir de imágenes del área auricular que otros métodos de evaluación visual. Este estudio sugiere que el aprendizaje profundo tiene ventajas en términos de clasificación por sexo en muestras tailandesas.

Impact of 3D vision on mental workload and laparoscopic performance in inexperienced subjects.

OBJECTIVE: To assess the effect of vision in three dimensions (3D) versus two dimensions (2D) on mental workload and laparoscopic performance during simulation-based training. MATERIALS AND METHODS: A prospective, randomized crossover study on inexperienced students in operative laparoscopy was conducted. Forty-six candidates executed five standardized exercises on a pelvitrainer with both vision systems (3D and 2D). Laparoscopy performance was assessed using the total time (in seconds) and the number of failed attempts. For workload assessment, the validated NASA-TLX questionnaire was administered. RESULTS: 3D vision improves the performance reducing the time (3D = 1006.08 ± 315.94 vs. 2D = 1309.17 ± 300.28; P < .001) and the total number of failed attempts (3D = .84 ± 1.26 vs. 2D = 1.86 ± 1.60; P < .001). For each exercise, 3D vision also shows better performance times: "transfer objects" (P = .001), "single knot" (P < .001), "clip and cut" (P < .05), and "needle guidance" (P < .001). Besides, according to the NASA-TLX results, less mental workload is experienced with the use of 3D (P < .001). However, 3D vision was associated with greater visual impairment (P < .01) and headaches (P < .05). CONCLUSION: The incorporation of 3D systems in laparoscopic training programs would facilitate the acquisition of laparoscopic skills, because they reduce mental workload and improve the performance on inexperienced surgeons. However, some undesirable effects such as visual discomfort or headache are identified initially.