Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms

In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning

Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.

Age Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneus

BACKGROUND

Determination of bone age is a critical issue for forensics, surgery, and basic sciences.

AIM

This study aims to estimate age with high accuracy and precision using Machine Learning (ML) algorithms with parameters obtained from calcaneus x-ray images of healthy individuals.

METHOD

The study was carried out by retrospectively examining the foot X-ray images of 341 people aged 18-65 years. Maximum width of the calcaneus (MW), body width (BW), maximum length (MAXL), minimum length (MINL), facies articularis cuboidea height (FACH), maximum height (MAXH), and tuber calcanei width (TKW) parameters were measured from the images. The measurements were then grouped as 20-45 years of age, 46-64 years of age, 65 and older, and age estimation was made by using these at the input of ML models.

RESULTS

As a result of the ML input of the measurements obtained, a 0.85 Accuracy (Acc) rate was obtained with the Extra Tree Classifier algorithm. The accuracy rate of other algorithms was found to vary between 0.78 and 0.82. The contribution of parameters to the overall result was evaluated by using the shapley additive explanations (SHAP) analyzer of Random Forest algorithm and the MAXH parameter was found to have the highest contribution in age estimation.

CONCLUSIONS

As a result of our study, calcaneus bone was found to have high accuracy and precision in age estimations.

Anthropometric measurements of the foot cannot predict the screw diameter for fifth metatarsal fractures intramedullary fixation

PURPOSE

The present study aimed to evaluate the accuracy of anthropometric foot measurements in predicting the diameter of the intramedullary screw for fifth metatarsal fracture fixation. Secondary aim was to identify whether the fifth metatarsal intramedullary canal diameter is correlated to the fifth metatarsal length and the foot dimensions.

METHODS

In 29 cadaveric feet, the maximum length of the plantar surface of the foot (PL) and the perimeter of the foot at the level of the fifth metatarsal base (PBFM) were measured using a measuring tape. Subsequently, the fifth metatarsal was excised. Using Computed Tomography scan, the metatarsal length (FML), and the horizontal (HDI) and vertical diameter (VDI) at the isthmus level were measured. The HDI values were grouped in 5 mm increments to correspond to the recommended screw diameter (RSD) for intramedullary fixation. A univariate linear regression analysis considered RSD and HDI as the dependent variables and FML, PL, PBFM as the independent variables. A multivariate regression analysis was performed to examine the predictive value of the two anthropometric measurements. A p-value < 0.05 was considered statistically significant.

RESULTS

All six univariate analyses revealed that the dependent variable was significantly correlated with the independent variable. However, the multivariate regression models showed that the anthropometric measurements were not significantly correlated with the RSD and HDI.

CONCLUSION

The current study found an association between the fifth metatarsal intramedullary canal diameter and the fifth metatarsal length and foot anthropometric dimensions. However, the anthropometric measurements of the foot presented a low predictive value for the decision of an intramedullary screw diameter in the treatment of fractures of the base of the fifth metatarsal.