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SUMMARY: Since machine learning algorithms give more reliable results, they have been used in the field of health in recent years. The orbital variables give very successful results in classifying sex correctly. This research has focused on sex determination using certain variables obtained from the orbital images of the computerized tomography (CT) by using machine learning algorithms (ML). In this study 12 variables determined on 600 orbital images of 300 individuals (150 men and 150 women) were tested with different ML. Decision tree (DT), K-Nearest Neighbour (KNN), Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), and Naive Bayes (NB) algorithms of ML were used for unsupervised learning. Statistical analyses of the variables were conducted with Minitab® 21.2 (64-bit) program. ACC rate of NB, DT, KNN, and LR algorithms was found as % 83 while the ACC rate of LDA and RFC algorithms was determined as % 85. According to Shap analysis, the variable with the highest degree of effect was found as BOW. The study has determined the sex with high accuracy at the ratios of 0.83 and 0.85 through using the variables of the orbital CT images, and the related morphometric data of the population under question was acquired, emphasizing the racial variation.

Dado que los algoritmos de aprendizaje automático dan resultados más fiables, en los últimos años han sido utilizados en el campo de la salud. Las variables orbitales dan resultados muy exitosos a la hora de clasificar correctamente el sexo. Esta investigación se ha centrado en la determinación del sexo utilizando determinadas variables obtenidas a partir de las imágenes orbitales de la tomografía computarizada (TC) mediante el uso de algoritmos de aprendizaje automático (AA). En este estudio se probaron 12 variables determinadas en 600 imágenes orbitales de 300 individuos (150 hombres y 150 mujeres) con diferentes AA. Se utilizaron algoritmos de AA de árbol de decisión (DT), K-Nearest Neighbour, regresión logística (RL), Random Forest (RF), análisis discriminante lineal (ADL) y Naive Bayes (NB) para el aprendizaje no supervisado. Los análisis estadísticos de las variables se realizaron con el programa Minitab® 21.2 (64 bits). La tasa de ACC de los algoritmos NB, DT, KNN y RL se encontró en % 83, mientras que la tasa de ACC de los algoritmos ADL y RFC se determinó en % 85. Según el análisis de Sharp, la variable con el mayor grado de efecto se encontró como BOW. El estudio determinó el sexo con alta precisión en las proporciones de 0,83 y 0,85 mediante el uso de las variables de las imágenes de TC orbitales, y se adquirieron los datos morfométricos relacionados de la población en cuestión, enfatizando la variación racial.

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SUMMARY: In the study, it was aimed to predict sex from hand measurements using machine learning algorithms (MLA). Measurements were made on MR images of 60 men and 60 women. Determined parameters; hand length (HL), palm length (PL), hand width (HW), wrist width (EBG), metacarpal I length (MIL), metacarpal I width (MIW), metacarpal II length (MIIL), metacarpal II width (MIIW), metacarpal III length (MIIL), metacarpal III width (MIIIW), metacarpal IV length (MIVL), metacarpal IV width (MIVW), metacarpal V length (MVL), metacarpal V width (MVW), phalanx I length (PILL), measured as phalanx II length (PIIL), phalanx III length (PIIL), phalanx IV length (PIVL), phalanx V length (PVL). In addition, the hand index (HI) was calculated. Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), K-nearest neighbour (KNN) and Naive Bayes (NB) were used as MLAs. In the study, the KNN algorithm's Accuracy, SEN, F1 and Specificity ratios were determined as 88 %. In this study using MLA, it is understood that the highest accuracy belongs to the KNN algorithm. Except for the hand's MIIW, MIIIW, MIVW, MVW, HI variables, other variables were statistically significant in terms of sex difference.

En el estudio, el objetivo era predecir el sexo a partir de mediciones manuales utilizando algoritmos de aprendizaje automático (MLA). Las mediciones se realizaron en imágenes de RM de 60 hombres y 60 mujeres. Parámetros determinados; longitud de la mano (HL), longitud de la palma (PL), ancho de la mano (HW), ancho de la muñeca (EBG), longitud del metacarpiano I (MIL), ancho del metacarpiano I (MIW), longitud del metacarpiano II (MIIL), ancho del metacarpiano II (MIIW), longitud del metacarpiano III (MIIL), ancho del metacarpiano III (MIIIW), longitud del metacarpiano IV (MIVL), ancho del metacarpiano IV (MIVW), longitud del metacarpiano V (MVL), ancho del metacarpiano V (MVW), longitud de la falange I (PILL), medido como longitud de la falange II (PIIL), longitud de la falange III (PIIL), longitud de la falange IV (PIVL), longitud de la falange V (PVL). Además, se calculó el índice de la mano (HI). Regresión logística (LR), Random Forest (RF), Análisis discriminante lineal (LDA), K-vecino más cercano (KNN) y Naive Bayes (NB) se utilizaron como MLA. En el estudio, las proporciones de precisión, SEN, F1 y especificidad del algoritmo KNN se determinaron en un 88 %. En este estudio que utiliza MLA, se entiende que la mayor precisión pertenece al algoritmo KNN. Excepto por las variables MIIW, MIIIW, MIVW, MVW, HI de la mano, otras variables fueron estadísticamente significativas en términos de diferencia de sexo.

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INTRODUCTION: The palmaris longus muscle displays a great variation in terms of incidence and shape. This study has documented the incidence ratio of this muscle among the students at Bolu Abant Izzet Baysal University, Faculty of Medicine, and has revealed the effects of its presence on wrist proprioception and grip strength. METHODS: A total of 101 students between the ages of 18 and 25 were included in the study. Age, height, weight, body mass index (BMI), and dominant upper extremity of the individuals were recorded. After the presence of palmaris longus tendon (PLT) was determined by using the Schaeffer's test, wrist proprioception was evaluated by using a digital inclinometer, and grip strength was evaluated by using a hand dynamometer. RESULTS: PLT absence rates were evaluated separately as right and left, and it was found as 16.8% and 17.8%. No correlation was found between the dominant upper extremity and BMI and the presence of PLT. The presence or absence of PLT has no effect on grip strength and wrist proprioception.  Conclusion: PLT is used in many clinical areas, such as reconstructive and cosmetic surgery, graft applications, tendon repairs, ptosis correction operations, and ligament stabilization. We think there will be no significant loss in the sense of proprioception and grip strength in the absence of PLT.

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To find an oblique cutting plane of a plastinate, e.g. to cut gamma-nails in the femur, the Block Plastination technique was modified. After CT and MRI examination, the specimens were plastinated with the standard resin mixture E6/E12/E600. Instead of using a box to form a block during the casting and curing stage, we embedded the specimen in a sac made of polyester foil. A polymerized wooden block was attached to the specimen. The sac was wrapped with tape to the embedded specimen with the block. This approach limited the amount of required resin to the inner volume of the plastinate. Then, the plastination sac was put in the incubator for further polymerization and curing. When the foil was removed from the plastinated specimen, the wooden block served as a socket for the grip when sawing. The outer shape of the specimen remained visible. Doing so, the adequate cutting plane could be determined easily.

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