Palatal canine impaction is associated with craniofacial shape in humans.

BACKGROUND/OBJECTIVES: It is unclear whether palatal canine impaction is related to genetic or local/environmental factors. If a genetic origin is assumed, then it could be expected that palatal canine impaction is associated with overall craniofacial development. Within this context, the aim of this study was to evaluate the craniofacial morphology of individuals with palatal canine impaction and compare it to a matched group of normal controls. MATERIALS/METHODS: The sample for this investigation comprised 404 individuals (232 females and 172 males). Half of these individuals presented with unilateral or bilateral palatal canine impaction confirmed clinically and radiographically. The other half were matched for sex and age with the first half and comprised individuals without tooth impaction, apart from third molars. The shape of the craniofacial structures was outlined on calibrated cephalometric images through 15 curves and 127 landmarks (11 fixed and 116 semi-landmarks). Shape configurations were superimposed using Procrustes Superimposition and the resulting shape coordinates were reduced into principal components for all subsequent analyses. The effect of palatal canine impaction on craniofacial shape was assessed with regression models, separately in females and males. All statistical tests were performed assuming a type-1 error of 5%. RESULTS: Individuals with palatally impacted canines appear to have a less convex face, a more brachyfacial skeletal pattern, and a sagittally extended premaxilla. In females effect sizes ranged between η2 = 0.136-0.397 (P < 0.05) and in males between η2 = 0.125-0.396 (P < 0.05, apart from the entire craniofacial configuration: P = 0.259). LIMITATIONS: Palatal canine impaction was not confirmed through cone beam computer tomography images in all patients, however, in those cases, the treatment history confirmed the diagnosis. CONCLUSIONS/IMPLICATIONS: Palatal canine impaction is related to a distinct craniofacial shape in females and males. These findings allow for speculation that palatal canine impaction is affected by genetic pathways involved in overall craniofacial development.

Registration accuracy comparing different rendering techniques on local vs external virtual 3D liver model reconstruction for vascular landmark setting by intraoperative ultrasound in augmented reality navigated liver resection.

PURPOSE: Augmented reality navigation in liver surgery still faces technical challenges like insufficient registration accuracy. This study compared registration accuracy between local and external virtual 3D liver models (vir3DLivers) generated with different rendering techniques and the use of the left vs right main portal vein branch (LPV vs RPV) for landmark setting. The study should further examine how registration accuracy behaves with increasing distance from the ROI. METHODS: Retrospective registration accuracy analysis of an optical intraoperative 3D navigation system, used in 13 liver tumor patients undergoing liver resection/thermal ablation. RESULTS: 109 measurements in 13 patients were performed. Registration accuracy with local and external vir3DLivers was comparable (8.76 ± 0.9 mm vs 7.85 ± 0.9 mm; 95% CI = -0.73 to 2.55 mm; p = 0.272). Registrations via the LPV demonstrated significantly higher accuracy than via the RPV (6.2 ± 0.85 mm vs 10.41 ± 0.99 mm, 95% CI = 2.39 to 6.03 mm, p < 0.001). There was a statistically significant positive but weak correlation between the accuracy (dFeature) and the distance from the ROI (dROI) (r = 0.298; p = 0.002). CONCLUSION: Despite basing on different rendering techniques both local and external vir3DLivers have comparable registration accuracy, while LPV-based registrations significantly outperform RPV-based ones in accuracy. Higher accuracy can be assumed within distances of up to a few centimeters around the ROI.

Development and validation of predictive models for skeletal malocclusion classification using airway and cephalometric landmarks.

OBJECTIVE: This study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images. BACKGROUND: In orthodontics, multitudinous studies have reported the correlation between orthodontic treatment and changes in the anatomy as well as the functioning of the airway. Typically, the values obtained from various measurements of cephalometric landmarks are used to determine skeletal class based on the interpretation an orthodontist experiences, which sometimes may not be accurate. METHODS: Samples of skeletal anatomical data were retrospectively obtained and recorded in Digital Imaging and Communications in Medicine (DICOM) file format. The DICOM files were used to reconstruct 3D models using 3DSlicer (slicer.org) by thresholding airway regions to build up 3D polygon models of airway regions for each sample. The 3D models were measured for different landmarks that included measurements across the nasopharynx, the oropharynx, and the hypopharynx. Male and female subjects were combined as one data set to develop supervised learning models. These measurements were utilized to build 7 artificial intelligence-based supervised learning models. RESULTS: The supervised learning model with the best accuracy was Random Forest, with a value of 0.74. All the other models were lower in terms of their accuracy. The recall scores for Class I, II, and III malocclusions were 0.71, 0.69, and 0.77, respectively, which represented the total number of actual positive cases predicted correctly, making the sensitivity of the model high. CONCLUSION: In this study, it is observed that the Random Forest model was the most accurate model for predicting the skeletal malocclusion based on various airway and cephalometric landmarks.

Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study.

BACKGROUND: Accurate assessment of basal bone width is essential for distinguishing individuals with normal occlusion from patients with maxillary transverse deficiency who may require maxillary expansion. Herein, we evaluated the effectiveness of a deep learning (DL) model in measuring landmarks of basal bone width and assessed the consistency of automated measurements compared to manual measurements. METHODS: Based on the U-Net algorithm, a coarse-to-fine DL model was developed and trained using 80 cone-beam computed tomography (CBCT) images. The model's prediction capabilities were validated on 10 CBCT scans and tested on an additional 34. To evaluate the performance of the DL model, its measurements were compared with those taken manually by one junior orthodontist using the concordance correlation coefficient (CCC). RESULTS: It took approximately 1.5 s for the DL model to perform the measurement task in only CBCT images. This framework showed a mean radial error of 1.22 ± 1.93 mm and achieved successful detection rates of 71.34%, 81.37%, 86.77%, and 91.18% in the 2.0-, 2.5-, 3.0-, and 4.0-mm ranges, respectively. The CCCs (95% confidence interval) of the maxillary basal bone width and mandibular basal bone width distance between the DL model and manual measurement for the 34 cases were 0.96 (0.94-0.97) and 0.98 (0.97-0.99), respectively. CONCLUSION: The novel DL framework developed in this study improved the diagnostic accuracy of the individual assessment of maxillary width. These results emphasize the potential applicability of this framework as a computer-aided diagnostic tool in orthodontic practice.

Evaluating cranial landmarks for yaw orientation in natural head position: a 3D study.

OBJECTIVES: To assess the symmetry of various cranial anthropometric points used as references for yaw orientation in the natural head position (NHP), relative to the mid-sagittal plane. MATERIALS AND METHODS: A prospective analysis using tomography data from 55 patients was conducted. Radiopaque markers, placed on patients in NHP, facilitated head position recording in three planes, with subsequent digital transfer for orientation analysis. Symmetry of eight points (zygomaxillare, zygion, ectoconchion, frontozygomatic suture, stephanion, porion, mastoidale, condylion laterale) was measured against the mid-sagittal plane. RESULTS: Significant asymmetry was observed in the stephanion, frontozygomatic suture, and ectoconchion points (p < 0.05). No significant differences were found in the symmetry of other points (p > 0.05). CONCLUSIONS: Findings suggest that stephanion, frontozygomatic suture, and ectoconchion are unreliable for yaw orientation in NHP. Other points, combined with clinical measurements, may offer better reliability.

Retrospective analysis of the upper airway anatomy and Sella turcica morphology across different skeletal malocclusions: a computerized technique.

OBJECTIVE: This study aimed to investigate the normal volumetric space and variations in the measurements of different landmarks in adults with different skeletal relations of the maxilla and the mandible based on CBCT data. The study also analyses these landmarks to locate any correlations. BACKGROUND: Numerous studies in orthodontics have found a relationship between orthodontic treatment and changes in the anatomy and function of the airway. Severe changes in airway morphology can cause breathing difficulties, lower quality of life, and even result in life-threatening conditions such as obstructive sleep apnoea. Consequently, orthodontic diagnosis and treatment planning require a thorough understanding of the airway space and its function. METHODS: The present retrospective study was conducted using CBCT records of 120 adult patients, containing 40 samples of each skeletal class (20 males and 20 females). The boundaries were defined for the 3 major regions: the nasopharynx, the oropharynx, and the hypopharynx. Various measurements were recorded across these regions, as well as selective cephalometric landmarks. The obtained data was used to calculate average and standard deviation, while regression analysis was used to evaluate correlations and t-test was used to test statistical significance of gender differences. RESULTS: The results demonstrate that skeletal Class III individuals exhibit a reduced airway volume in the nasopharynx compared to other groups, whereas skeletal Class II individuals displayed a diminished airway volume in the hypopharynx. A strong correlation was observed for Sella turcica parameters. There were no significant differences in skeletal parameters across genders. Nasopharynx cavity volume demonstrated significant differences between skeletal Class I-Class III as well as between skeletal Class II-Class III. Hypopharynx cavity volume also demonstrated significant differences between skeletal Class I-Class II and between skeletal Class II-Class III. CONCLUSION: The major findings are the presence of a reduced nasopharyngeal volume in skeletal Class III malocclusions while skeletal Class II individuals displayed a diminished hypopharyngeal volume, making these critical areas to consider during the diagnostic and orthodontic treatment planning stages. This study also revealed a consistent correlation between Sella turcica parameters across various facial skeletal profiles, with skeletal Class II patients exhibiting a distinct pattern and skeletal Class I and Class III demonstrating an average relationship.

Automatic point detection on cephalograms using convolutional neural networks: A two-step method.

This project aimed to develop an artificial intelligence program tailored for cephalometric images. The program employs a convolutional neural network with 6 convolutional layers and 2 affine layers. It identifies 18 key points on the skull to compute various angles essential for diagnosis. Utilizing a custom-built desktop computer with a moderately priced graphics processing unit, cephalogram images were resized to 800×800 pixels. Training data comprised 833 images, augmented 100 times; an additional 179 images were used for testing. Due to the complexity of training with full-size images, training was divided into two steps. The first step reduced images to 128×128 pixels, recognizing all 18 points. In the second step, 100×100 pixels blocks were extracted from original images for individual point training. The program then measured six angles, achieving an average error of 3.1 pixels for the 18 points, with SNA and SNB angles showing an average difference of less than 1°.

Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.

OBJECTIVES: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. MATERIALS AND METHODS: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. RESULTS: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. CONCLUSIONS: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

Comparison of ultrasound- vs. landmark-guided injections for musculoskeletal pain: an umbrella review.

OBJECTIVE: This umbrella review synthesizes systematic reviews and meta-analyses to reach a conclusion concerning the overall effectiveness of ultrasound-guided vs landmark-guided injections for treating musculoskeletal pain. DESIGN: Umbrella review. METHODS: PubMed, EMBASE, MEDLINE, and Web of Science were searched for relevant systematic reviews and meta-analyses from inception to March 2024. Critical appraisal, data extraction, and synthesis were performed in accordance with the criteria for conducting an umbrella review. RESULTS: Seventeen articles, comprising 4 systematic reviews and 13 meta-analyses, were included. Using the AMSTAR2 instrument for quality assessment, 3 articles were rated as high quality, 1 as moderate, 7 as low, and 6 as critically low. Generally, ultrasound-guided injections were found to be more accurate than landmark-guided injections, particularly in the shoulder joint, though the results for pain relief and functional outcomes varied. Ultrasound guidance was notably effective for injections into the bicipital groove, wrist, hip, and knee - yielding greater accuracy and improved pain management. Both ultrasound-guided and landmark-guided techniques showed low incidence of adverse effects. CONCLUSION: This umbrella review offers an in-depth analysis of the comparative effectiveness of ultrasound-guided and landmark-guided injections across a range of musculoskeletal sites/conditions. The findings suggest that ultrasound-guided is a reliable method.

Facial asymmetry outcome of orthognathic surgery in mild craniofacial microsomia compared to non-syndromic class II asymmetry.

OBJECTIVES: To compare the facial asymmetry after bimaxillary surgery between mild craniofacial microsomia (CFM) and non-syndromic class II asymmetry. MATERIALS AND METHODS: Cone-beam computed tomography scans of adults with Pruzansky-Kaban types I and IIA CFM (CFM groups, n = 20), non-syndromic skeletal class II asymmetry (Class II group, n = 20), and normal controls (control group, n = 20) were compared. The area asymmetry of lower face and jaw bones was quantified. Landmark-based method was used to evaluate the lower facial asymmetry regarding midline, cants, and contour. RESULTS: There were no significant postoperative differences in the hemi-facial and hemi-jaw area asymmetry between CFM and Class II groups, both of which were significantly larger than the control group. No significant difference was found in the midline deviation and lip and occlusal cants between CFM and Class II groups. The vertical contour asymmetry in CFM group became significantly larger than Class II group. Compared to the control group, the deviation of pronasale, subnasale, and soft-tissue menton, lip and occlusal cants, and sagittal and vertical contour asymmetry in CFM group were significantly larger, as were the deviation of subnasale and soft-tissue menton and vertical contour asymmetry in Class II group. CONCLUSIONS: The vertical contour asymmetry of mild CFM was significantly larger than non-CFM class II after surgery, while the area asymmetry, midline deviation, cants, and sagittal contour asymmetry of lower face showed no significant difference. CLINICAL RELEVANCE: Be aware that correcting vertical asymmetry of contour, lip, and dentition in CFM is still challenging.

Optimising central venous catheter placement by comparing cavoatrial junction position to chest X-ray landmarks: A cross-sectional study using CT chest reconstruction.

INTRODUCTION: Central venous catheter (CVC) tip placement guided by chest X-ray (CXR) landmarks is currently prone to inconsistency and malpositioning. This study aims to better define the relationship between the cavoatrial junction (CAJ) and selected X-ray landmarks. METHODS: Chest CTs of 100 patients were retrospectively assessed. CT images were converted to a 'virtual CXR' using a digital workstation, enabling simultaneous localisation of the CAJ and evaluation of CXR landmarks. Vertical distances between the CAJ and selected landmarks were measured for each patient. Measurements were assessed for correlation with age and compared between age groups and sexes. RESULTS: The mean vertical distance of the following landmarks above the CAJ was found: the carina (46.2 mm), the intersection of the bronchus intermedius and the right heart border (7.6 mm) and the superior inflection of the right heart border (Sup-RHB) (13.0 mm). The maximum lateral bulge of the right heart border (Lat-RHB) was 18.4 mm below the CAJ. A new landmark: the mid-superior right heart border, defined as the mid-point between the Sup-RHB and Lat-RHB, was the closest to the CAJ, lying 2.6 mm below the CAJ. CONCLUSION: We propose that the CVC tip can be placed at the mid-superior right heart border landmark.

Quantitative analysis of surgical landmarks of the face in fixed cadaveric heads: clinical and anatomical implications.

PURPOSE: This study aimed to examine and analyze the presence of sexual dimorphism and symmetry/asymmetry in fixed cadaveric heads and their contributions to clinical practice. METHODS: Measurements were conducted on 6 cadavers and 24 fixed cadaveric heads (n = 30) using a digital microcaliper to assess cranial dimensions, including surgical landmarks, facial index (FI), orbital index (OI), and the danger triangle of the face, offering crucial references for surgeons during surgical interventions. RESULTS: Analyses were conducted on cadavers consisting of 10 females (33.3%) and 20 males (66.7%). Males demonstrated significantly higher values than females in terms of Zy-Zy (Zy = Zygion), nasal body, and width/length of the columella. Hyperleptoprosop types were the most frequently observed, followed by the leptoprosop type. Females exhibited megaseme characteristics according to OI on both sides. Additionally, the danger triangle of the face was greater in males compared than in females, suggesting a higher risk of infection in this region among males. CONCLUSION: The observed sex differences in facial and nasal dimensions, along with facial indices, are crucial for customizing surgical procedures to individual patients. For example, the greater distance in the danger triangle among males highlights the importance of considering anatomical variations to avoid complications, such as septic cavernous sinus thrombosis. Integrating these measurements into preoperative planning can enhance the precision of facial reconstruction and aesthetic surgeries, thereby improving patient outcomes and safety.

Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning.

BACKGROUND: Postoperative pancreatic fistula (POPF) is a critical complication of laparoscopic gastrectomy (LG). However, there are no widely recognized anatomical landmarks to prevent POPF during LG. This study aimed to identify anatomical landmarks related to POPF occurrence during LG for gastric cancer and to develop an artificial intelligence (AI) navigation system for indicating these landmarks. METHODS: Dimpling lines (DLs)-depressions formed between the pancreas and surrounding organs-were defined as anatomical landmarks related to POPF. The DLs for the mesogastrium, intestine, and transverse mesocolon were named DMP, DIP, and DTP, respectively. We included 50 LG cases to develop the AI system (45/50 were used for training and 5/50 for adjusting the hyperparameters of the employed system). Regarding the validation of the AI system, DLs were assessed by an external evaluation committee using a Likert scale, and the pancreas was assessed using the Dice coefficient, with 10 prospectively registered cases. RESULTS: Six expert surgeons confirmed the efficacy of DLs as anatomical landmarks related to POPF in LG. An AI system was developed using a semantic segmentation model that indicated DLs in real-time when this system was synchronized during surgery. Additionally, the distribution of scores for DMP was significantly higher than that of the other DLs (p < 0.001), indicating the relatively high accuracy of this landmark. In addition, the Dice coefficient of the pancreas was 0.70. CONCLUSIONS: The DLs may be used as anatomical landmarks related to POPF occurrence. The developed AI navigation system can help visualize the DLs in real-time during LG.

A Survey Study of the 3D Facial Landmark Detection Techniques Used as a Screening Tool for Diagnosis of the Obstructive Sleep Apnea Syndrome.

Obstructive Sleep Apnea (OSA) is a common disorder affecting both adults and children. It is characterized by repeated episodes of apnea (stopped breathing) and hypopnea (reduced breathing), which result in intermittent hypoxia. We recognize pediatric and adult OSA, and this paper focuses on pediatric OSA. While adults often suffer from daytime sleepiness, children are more likely to develop behavioral abnormalities. Early diagnosis and treatment are important to prevent negative effects on children's development. Without the treatment, children may be at increased risk of developing high blood pressure or other heart problems. The gold standard for OSA diagnosis is the polysomnography (sleep study) PSG performed at a sleep center. Not only is it an expensive procedure, but it can also be very stressful, especially for children. Patients have to stay at the sleep center during the night. Therefore, screening tools are very important. Multiple studies have shown that OSA screening tools can be based on facial anatomical landmarks. Anatomical landmarks are landmarks located at specific anatomical locations. For the purpose of the screening tool, a specific list of anatomical locations needs to be identified. We are presenting a survey study of the automatic identification of these landmarks on 3D scans of the patient's head. We are considering and comparing both knowledge-based and AI-based identification techniques, with a focus on the development of the automatic OSA screening tool.

Network analysis of three-dimensional hard-soft tissue relationships in the lower 1/3 of the face: skeletal Class I-normodivergent malocclusion versus Class II-hyperdivergent malocclusion.

BACKGROUND: The determining effect of facial hard tissues on soft tissue morphology in orthodontic patients has yet to be explained. The aim of this study was to clarify the hard-soft tissue relationships of the lower 1/3 of the face in skeletal Class II-hyperdivergent patients compared with those in Class I-normodivergent patients using network analysis. METHODS: Fifty-two adult patients (42 females, 10 males; age, 26.58 ± 5.80 years) were divided into two groups: Group 1, 25 subjects, skeletal Class I normodivergent pattern with straight profile; Group 2, 27 subjects, skeletal Class II hyperdivergent pattern with convex profile. Pretreatment cone-beam computed tomography and three-dimensional facial scans were taken and superimposed, on which landmarks were identified manually, and their coordinate values were used for network analysis. RESULTS: (1) In sagittal direction, Group 2 correlations were generally weaker than Group 1. In both the vertical and sagittal directions of Group 1, the most influential hard tissue landmarks to soft tissues were located between the level of cemento-enamel junction of upper teeth and root apex of lower teeth. In Group 2, the hard tissue landmarks with the greatest influence in vertical direction were distributed more forward and downward than in Group 1. (2) In Group 1, all the correlations for vertical-hard tissue to sagittal-soft tissue position and sagittal-hard tissue to vertical-soft tissue position were positive. However, Group 2 correlations between vertical-hard tissue and sagittal-soft tissue positions were mostly negative. Between sagittal-hard tissue and vertical-soft tissue positions, Group 2 correlations were negative for mandible, and were positive for maxilla and teeth. CONCLUSION: Compared with Class I normodivergent patients with straight profile, Class II hyperdivergent patients with convex profile had more variations in soft tissue morphology in sagittal direction. In vertical direction, the most relevant hard tissue landmarks on which soft tissue predictions should be based were distributed more forward and downward in Class II hyperdivergent patients with convex profile. Class II hyperdivergent pattern with convex profile was an imbalanced phenotype concerning sagittal and vertical positions of maxillofacial hard and soft tissues.

Skeletal muscle quantity and quality evaluation in heart failure: comparing thoracic versus abdominopelvic CT approaches.

Given the critical role of skeletal muscle in healthy aging, low muscle mass (myopenia) and quality (myosteatosis) can be used as predictors of poor functional and cardiometabolic outcomes. Myopenia is also a part of sarcopenia and malnutrition diagnostic criteria. However, there is limited evidence for using chest computed tomography (CT) to evaluate muscle health. We aimed to compare chest CT landmarks to the widely used L3 vertebra for single-slice skeletal muscle evaluation in patients with heart failure (HF). Patients admitted for acute decompensated HF between January 2017 and December 2018 were retrospectively analyzed. Body composition measurements were made on CT of the chest and abdomen/pelvis with or without contrast one month before discharge. Skeletal muscle index (SMI) and intermuscular adipose tissue percentage (IMAT%) were calculated at several thoracic levels (above the aortic arch, T8, and T12) and correlated to the widely used L3 level. A total of 200 patients were included, 89 (44.5%) female. The strongest correlation of thoracic SMI (for muscle quantity) and IMAT% (for muscle quality) with L3 was at the T12 level (r = 0.834, p < 0.001 and r = 0.757, p < 0.001, respectively). Cutoffs to identify low muscle mass for T12 SMI (derived from the lowest sex-stratified L3 SMI tertile) were 31.1 cm²/m² in men and 26.3 cm²/m² in women. SMI and IMAT% at T12 had excellent correlations with the widely used L3 level for muscle quantity and quality evaluation in patients with HF.

Osteon shape variation in the femoral diaphysis: A geometric-morphometric approach on human cortical bone microstructure in an elderly sample.

Geometric morphometrics (GMM) have been applied to understand morphological variation in biological structures. However, research studying cortical bone through geometric histomorphometrics (GHMM) is scarce. This research aims to develop a landmark-based GHMM protocol to depict osteonal shape variation in the femoral diaphysis, exploring the role of age and biomechanics in bone microstructure. Proximal, midshaft, and distal anatomical segments from the femoral diaphysis of six individuals were assessed, with 864 secondary intact osteons from eight periosteal sampling areas being manually landmarked. Observer error was tested using Procrustes ANOVA. Average osteonal shape and anatomical segment-specific variation were explored using principal component analysis. Osteon shape differences between segments were examined using canonical variate analysis (CVA). Sex differences were assessed through Procrustes ANOVA and discriminant function analysis (DFA). The impact of osteonal size on osteonal shape was investigated. High repeatability and reproducibility in osteon shape landmarking were reported. The average osteon shape captured was an elliptical structure, with PC1 reflecting more circular osteons. Significant differences in osteon shape were observed between proximal and distal segments according to CVA. Osteon shape differed between males and females, with DFA showing 52% cross-validation accuracies. No effect of size on shape was reported. Osteonal shape variation observed in this study might be explained by the elderly nature of the sample as well as biomechanical and physiological mechanisms playing different roles along the femoral diaphysis. Although a larger sample is needed to corroborate these findings, this study contributes to the best of our knowledge on human microanatomy, proposing a novel GHMM approach.

Sihler's staining of the anterior belly of digastric muscle for botulinum toxin injection.

PURPOSE: The anterior belly of the digastric muscle (ABDM) is the target of botulinum toxin injection; however, anatomical considerations related to the injection point are absent. This study used Sihler's staining to analyze the intramuscular nerve distribution of ABDM to identify the most effective botulinum toxin injection points. METHODS: We used 12 specimens from 6 embalmed cadavers in this study. The specimens were manually dissected to preserve the mylohyoid nerve and subjected to Sihler's staining. From the gnathion to and hyoid bone, the ABDM was divided into three equal parts, distinguishing the anterior, middle, and posterior thirds. RESULTS: Only a branch of the mylohyoid nerve entered the ABDM, and its entry point was located in the middle-third region in all cases. The nerve endings were concentrated in the middle third (100%), followed by the anterior third (58.3%) and were not observed in the posterior third. CONCLUSION: The landmarks used in this study (gnathion and hyoid bone) are easily palpable on the skin surface, allowing clinicians to target the most effective injection site (middle third of ABDM). These results provide scientific and anatomic evidence for injection points, and will aid in the management of ABDM injection procedures in clinical practice.