Cadaveric study investigating the femoral nerve-sparing volume for pericapsular nerve group (PENG) block.

BACKGROUND: This 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. METHODS: In 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. RESULTS: A 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). CONCLUSION: For 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.

Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study.

Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.

Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population.

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.

Estimating age from digital radiographic images of lumbar vertebrae in a Thai population using an image analysis technique.

Using the lumbar vertebra for age estimation is helpful in cases when skeletal remains are incomplete and typical skeletal age indicators are absent. This study aimed to apply an image analysis method in extracting black pixel variables for age estimation by using the radiographic images of lumbar vertebra in a Thai population. All lumbar vertebrae L1-L5 of 220 (110 males and 110 females) from Thai individuals of known sex and ages were studied. The variables of Total Percentage of black pixels (TP), Mean Percentage of black pixels (MP), and Ratio of black to white pixels (BW), were calculated to assess the relationship between black pixel variables and aging. Equations were formulated using linear regression analysis. The results of this study indicated three variables of the lumbar vertebrae had significantly positive correlations with age. The correlation between parameters with age in males ranged 0.211-0.419, while the range in females was 0.219-0.458. The appropriate linear regression equation with the total and mean percentages of black pixel variables shows Age = -1.348+0.871 (TP) +0.514 (MP) of L4 for males (SEE; 15.4 years), and Age = 5.338 +0.316 (TP) +0.952 (MP) of L1 for females (SEE; 13.8 years). Age estimation using an image analysis method is an alternative to investigating the trabecular structure. The black pixel variable is not the actual value of bone density. However, it is useful to study its relationship with aging.