Sex determination is a fundamental step in biological profile estimation from skeletal remains in forensic anthropology. This study proposes deep learning and morphometric technique to perform sex determination from lumbar vertebrae in a Thai population. A total of 1100 lumbar vertebrae (L1-L5) from 220 Thai individuals (110 males and 110 females) were obtained from the Forensic Osteology Research Center, Faculty of Medicine, Chiang Mai University, Thailand. In addition, two linear measurements of superior and inferior endplates from the digital caliper and image analysis were carried out for morphometric technique. Deep learning applied image classification to the superior and inferior endplates of the lumbar vertebral body. All lumbar vertebrae images are included in the dataset to increase the number of images per class. The accuracy determined the performance of each technique. The results showed the accuracies of 82.7%, 90.0%, and 92.5% for digital caliper, image analysis, and deep learning techniques, respectively. The lumbar vertebrae L1-L5 exhibit sexual dimorphism and can be used in sex estimation. Deep learning is more accurate in determining sex than the morphometric method. In addition, the subjectivity and errors in the measurement are decreased. Finally, this study presented an alternative approach to determining sex from lumbar vertebrae when the more traditionally used skeletal elements are incomplete or absent.

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BackgroundThis cadaveric study investigated the maximum effective volume of dye in 90% of cases (MEV90) required to stain the iliac bone between the anterior inferior iliac spine (AIIS) and the iliopubic eminence (IPE) while sparing the femoral nerve during the performance of pericapsular nerve group (PENG) block.MethodsIn cadaveric hemipelvis specimens, the ultrasound transducer was placed in a transverse orientation, medial and caudal to the anterior superior iliac spine in order to identify the AIIS, the IPE and the psoas tendon. Using an in-plane technique and a lateral-to-medial direction, the block needle was advanced until its tip contacted the iliac bone. The dye (0.1% methylene blue) was injected between the periosteum and psoas tendon. Successful femoral-sparing PENG block was defined as the non-staining of the femoral nerve on dissection. Volume assignment was carried out using a biased coin design, whereby the volume of dye administered to each cadaveric specimen depended on the response of the previous one. In case of failure (ie, stained femoral nerve), the next one received a lower volume (defined as the previous volume with a decrement of 2 mL). If the previous cadaveric specimen had a successful block (ie, non-stained femoral nerve), the next one was randomized to a higher volume (defined as the previous volume with an increment of 2 mL), with a probability of b=1/9, or the same volume, with a probability of 1–b=8/9.ResultsA total of 32 cadavers (54 cadaveric hemipelvis specimens) were included in the study. Using isotonic regression and bootstrap CI, the MEV90 for femoral-sparing PENG block was estimated to be 13.2 mL (95% CI: 12.0 to 20.0). The probability of a successful response was estimated to be 0.93 (95% CI: 0.81 to 1.00).ConclusionFor PENG block, the MEV90 of methylene blue required to spare the femoral nerve in a cadaveric model is 13.2 mL. Further studies are required to correlate this finding with the MEV90 of local anesthetic in live subjects.

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Determining sex is a critical process in estimating biological profiles from skeletal remains. The clavicle is interesting in studying sex determination because it is durable to the environment, slow to decay, challenging to destroy, making the clavicle useful in autopsies and identification which can then lead to verification. The goal of this study was to use deep learning in determining sex from clavicles within the Thai population and obtain the accuracies for the validation set using a convolutional neural network (GoogLeNet). A total of 200 pairs of clavicles were obtained from 200 Thai persons (100 males and 100 females) as part of a training group. For the deep learning approach, the clavicle was photographed, and each clavicle image was submitted to the training model for sex determination. Training groups of 200 samples were made. Images of the same size were input into the training model. The percentage of the validation set accuracy was calculated from the MATLAB program. GoogLeNet was the best training model and get the result of validation set accuracy. The results of this study found accuracies for a validation set with the highest overall right lateral view of the clavicle with an accuracy of 95%. Accuracy from the validation set of each view of the clavicle can demonstrate the forensic value of sex determination. A deep learning approach with clavicles can determine the sex and is simple to utilize for forensic anthropology professionals.