METHODS: Thirty patients who underwent distal volar locking plate for distal radius fracture were included in a retrospective study. In all 30 patients no dorsal and intra-articular screw penetration were detected on standard AP and lateral views of a plain radiograph. CT scan of the operated wrist was performed to determine the number of intra-articular and dorsal screw penetrations. Clinical examination was performed to determine the wrist functions in comparison to the normal wrist.
RESULTS: Nineteen wrists were noted to have screw penetration either dorsally or intraarticularly. The highest incidence is in the 2nd extensor compartment where 13 screws had penetrated with a mean of 2.46 mm. Six screws penetrated into the distal radial ulnar joint and five screws into the wrist joint with a mean of 2.83 mm and 2.6 mm, respectively. However, there was no incidence of tendon irritation or rupture.
CONCLUSIONS: This study demonstrated a high incidence of dorsal and intra-articular screw penetration detected by CT scan which was not apparent in plain radiograph. We recommend that surgeons adhere to the principle of only near cortex fixation and downsizing the locking screw length by 2 mm.
PURPOSE: To examine the influence of physical activity (PA) and sedentary time on bone strength, structure, and density in older adolescents.
METHODS: We used peripheral quantitative computed tomography to estimate bone strength at the distal tibia (8% site; bone strength index, BSI) and tibial midshaft (50% site; polar strength strain index, SSIp) in adolescent boys (n = 86; 15.3 ± 0.4 years) and girls (n = 106; 15.3 ± 0.4 years). Using accelerometers (GT1M, Actigraph), we measured moderate-to-vigorous PA (MVPAAccel), vigorous PA (VPAAccel), and sedentary time in addition to self-reported MVPA (MVPAPAQ-A) and impact PA (ImpactPAPAQ-A). We examined relations between PA and sedentary time and bone outcomes, adjusting for ethnicity, maturity, tibial length, and total body lean mass.
RESULTS: At the distal tibia, MVPAAccel and VPAAccel positively predicted BSI (explained 6-7% of the variance, p
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.
AIM: To present a case of extradural temporal bone chondroblastoma and discuss the clinical presentation, radiographic findings, histology and particularly the surgical management of the case.
CASE REPORT: We report a case of a 31-year-old man who presented with a painless left temporal swelling and left sided hearing loss for four months. Computed tomography (CT) scan revealed an aggressive mass involving the left preauricular region with temporal mastoid bone erosion. Magnetic resonance imaging (MRI) showed an extra-axial left temporal mastoid mass pushing the left temporal lobe superiorly. The patient underwent complete excision of the temporal bone tumor. The final histopathological diagnosis was in keeping with chondroblastoma.
CONCLUSION: Temporal bone chondroblastoma is rare but an aggressive condition. Complete tumor resection via an appropriate approach that enables adequate exposure will lead to a favorable outcome.
METHODS: A six-year retrospective review at our institution on adult patients with TB and malignant-PPL diagnosed from rEBUS procedure from October 1, 2016, to December 31, 2022. Clinical, radiological, procedural, histological and microbiological data were extracted and analysed.
RESULTS: 387 PPLs were included in our cohort, 32 % were TB-PPL and 68 % were malignant-PPL. The median age was 63 (IQR 55-70) years, with the TB-PPL group significantly younger. The median size of the target lesion was 2.90 (IQR 2.26-4.00) cm. The overall rEBUS diagnostic yield was 85.3 %, with a 1.3 % pneumothorax risk. Multivariate analysis identified independent predictors for TB-PPL, including age <60 years (adj OR 2.635), target lesion size <2 cm (adj OR 2.385), upper lobe location (adj OR 2.020), presence of a cavity on pre-procedural CT (adj OR 4.186), and presence of rEBUS bronchogram (adj OR 2.722). These variables achieved an area under the curve of 0.729 (95 % CI 0.673-0.795) with a diagnostic accuracy of 75.49 % (95 % CI 70.68-79.88).
CONCLUSIONS: Despite non-specific radiological findings in TB-PPL, our study identifies younger age, target lesion size less than 2 cm, upper lobe location, the presence of cavitation, and rEBUS bronchogram were independent clinical predictors for TB-PPL. This prediction model potentially helps mitigate the risk of accidental TB exposure during bronchoscopic procedures. A future prospective cohort study to validate these findings is essential to allow proper triaging of patient planning for rEBUS procedure.
MATERIAL AND METHODS: The study included 223 tomograms of the head and neck in sagittal projection from patients without any pathology of the studied structures. Morphometric analysis was carried out using PjaPro and Gradient programs, statistical analysis was performed by SPSS Statistics software. A fully convolutional EfficientNet-B2 neural network was used, which was trained in two stages: selection of the area of interest and solution of regression tasks.
RESULTS: Morphometric assessment and subsequent statistical analysis of the selected group of features have shown presence of the strongest correlation with age in the indicator characterizing the involution of the median atlantoaxial joint. A deep learning method using the convolutional network, which automatically selects the desired area in the image (the area of the vertebral junction), classifies the sample, and makes an assumption about the age of the unknown individual with an accuracy of 7.5 to 10.5 years has been tested.
CONCLUSION: As a result of the study, a positive experience has been obtained indicating the possibility of using convolutional neural networks to determine the age of the unknown person, which expands the evidence base and provides new opportunities for determining group-wide personality traits in forensic medicine.