Infrarenal aortic diameter, aortoiliac bifurcation level and lumbar disc degenerative changes: a cross-sectional MR study

Relationship of the lumbar Lordotic angle to the abdominal aortic deviation and abdominal aortic diameter

In anterior approaches during lumbar disc surgery, knowledge of abdominal aorta (AA) deviation and the lumbar lordotic angle (LLA) may provide surgeons with greater foresight in surgical planning and assist in preventing complications. This study aimed to determine the effect of AA deviation on AA morphometry and the effect of the LLA on both AA deviation and AA morphometry. A total of 499 individuals (259 females, 240 males) with a mean age of 50.60 ± 17.96 years were included in this retrospective study. On 2D computed tomography angiography (CTA) images, LLA and AA diameters were measured at 3 different vertebral levels. The vertebral levels of AA deviation were determined on 3D images. While the AA diameter values were greater in individuals with AA deviation (p < 0.001), it was observed that the level of deviation did not affect AA morphometry (p > 0.05). AA deviation was most frequently observed at the L2-L3 level in females, while the mid-L3 level was the most common site of AA deviation in males (p = 0.019). LLA was significantly greater in females compared to males (p = 0.015), but it was not affected by the presence of AA deviation (p = 0.373). The data from this study could provide guidance for endovascular interventions and anterior lumbosacral surgical procedures and could make a valuable contribution to the existing literature.

Influence of Aortic Deviation on Abdominal Aorta Bifurcation Level Relative to Vertebral Position

Introduction Research on abdominal aortic deviation (AAD) and its impact on the vertebral level of abdominal aortic bifurcation (AAB) has been limited. We aimed to determine the level of AAB with respect to vertebral levels and assess the proportion of AAD and its impact on AAB. Materials and methods This single-arm, cross-sectional, retrospective study involved contrast-enhanced computed tomography (CECT) scans of the abdomen and pelvis of 208 subjects aged 18 years or older. AAD and AAB in terms of vertebral levels were noted using the digital imaging and communication in medicine (DICOM) viewing software RadiAnt (Medixant, Poznań, Poland). Results The rate of AAD was found to be 44 out of 208 (21.2%), 16 out of 98 (16.3%) in men and 28 out of 110 (25.45%) in women (p=0.10). The AAD rates in the 31-40-, 51-60-, and 71-80-year age groups were 11 out of 37 (29.7%), 17 out of 61 (27.8%), and three out of seven (42.8%) with p=0.03, respectively. AAB was seen in the middle of the L4 vertebrae in 65 cases (31.2%), followed by 49 cases (23.6%) in the L4 lower vertebrae. Twenty-three (20.9%) women had AAB above the L4, compared to 13 (13.2%) men (p=0.34). AAB was located below the L4 vertebral level in eight out of 44 (18.18%) subjects with AAD versus 41 out of 164 (25%) subjects without AAD (p=0.55). Conclusion AAD was observed in approximately one-fifth of the subjects in our study cohort, with a higher proportion among women and in elderly age groups. AAB was most commonly observed in the middle of the L4 vertebrae and AAD did not impact AAB levels.

Osteoporotic vertebral compression fracture (OVCF) detection using artificial neural networks model based on the AO spine-DGOU osteoporotic fracture classification system

Background

Osteoporotic Vertebral Compression Fracture (OVCF) substantially reduces a person's health-related quality of life. Computer Tomography (CT) scan is currently the standard for diagnosis of OVCF. The aim of this paper was to evaluate the OVCF detection potential of artificial neural networks (ANN).

Methods

Models of artificial intelligence based on deep learning hold promise for quickly and automatically identifying and visualizing OVCF. This study investigated the detection, classification, and grading of OVCF using deep artificial neural networks (ANN). Techniques: Annotation techniques were used to segregate the sagittal images of 1,050 OVCF CT pictures with symptomatic low back pain into 934 CT images for a training dataset (89%) and 116 CT images for a test dataset (11%). A radiologist tagged, cleaned, and annotated the training dataset. Disc deterioration was assessed in all lumbar discs using the AO Spine-DGOU Osteoporotic Fracture Classification System. The detection and grading of OVCF were trained using the deep learning ANN model. By putting an automatic model to the test for dataset grading, the outcomes of the ANN model training were confirmed.

Results

The sagittal lumbar CT training dataset included 5,010 OVCF from OF1, 1942 from OF2, 522 from OF3, 336 from OF4, and none from OF5. With overall 96.04% accuracy, the deep ANN model was able to identify and categorize lumbar OVCF.

Conclusions

The ANN model offers a rapid and effective way to classify lumbar OVCF by automatically and consistently evaluating routine CT scans using AO Spine-DGOU osteoporotic fracture classification system.

Diabetes Mellitus and Intervertebral Disc Degeneration: A Meta-Analysis

BACKGROUND

Diabetes mellitus (DM) has been related to a higher risk of intervertebral disc degeneration (IVDD). However, the previous studies showed inconsistent results. We performed a systematic review and meta-analysis to comprehensively investigate the association between DM and IVDD in adult population.

METHODS

Observational studies relevant to the aim of the meta-analysis were retrieved by search of electronic databases including PubMed, Web of Science, and Embase. A random-effects model was used to combine the data by incorporating the influence of between-study heterogeneity.

RESULTS

Eleven observational studies involving 2,881,170 adults were included. Among them, 1,211,880 had DM. Compared to those with normoglycemia, patients with DM were associated with a higher odds ratio of IVDD (OR: 1.68, 95% confidence interval: 1.24 to 2.29, P<0.001; I2=98%). Further sensitivity analysis excluding database studies with IVDD diagnosed via International Classification of Diseases codes showed consistent results (odds ratio: 1.47, 95% confidence interval: 1.06 to 2.02, P=0.02) with no statistical heterogeneity (I2=0%). Subgroup analyses showed a stronger association between DM and IVDD in cohort studies than that in cross-sectional studies, in studies evaluating overall IVDD than that evaluating lumbar disc degeneration, and in studies that adjusted age and body mass index than that did not (P for subgroup differences all <0.05). Subgroup analyses according to study country and quality score did not significantly affect the association.

CONCLUSIONS

DM may be associated with IVDD in adult population, which seems to be independent of age and body mass index.

Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model

BACKGROUND AND OBJECTIVES

Intervertebral disc degeneration (IDD) is a common cause of symptomatic axial low back pain. Magnetic resonance imaging (MRI) is currently the standard for the investigation and diagnosis of IDD. Deep learning artificial intelligence models represent a potential tool for rapidly and automatically detecting and visualizing IDD. This study investigated the use of deep convolutional neural networks (CNNs) for the detection, classification, and grading of IDD.

METHODS

Sagittal images of 1000 IDD T2-weighted MRI images from 515 adult patients with symptomatic low back pain were separated into 800 MRI images using annotation techniques to create a training dataset (80%) and 200 MRI images to create a test dataset (20%). The training dataset was cleaned, labeled, and annotated by a radiologist. All lumbar discs were classified for disc degeneration based on the Pfirrmann grading system. The deep learning CNN model was used for training in detecting and grading IDD. The results of the training with the CNN model were verified by testing the grading of the dataset using an automatic model.

RESULTS

The training dataset of the sagittal intervertebral disc lumbar MRI images found 220 IDDs of grade I, 530 of grade II, 170 of grade III, 160 of grade IV, and 20 of grade V. The deep CNN model was able to detect and classify lumbar IDD with an accuracy of more than 95%.

CONCLUSION

The deep CNN model can reliably automatically grade routine T2-weighted MRIs using the Pfirrmann grading system, providing a quick and efficient method for lumbar IDD classification.

Ionizing radiation protection and the linear No-threshold controversy: Extent of support or counter to the prevailing paradigm

This study has developed a relationship that categorized radiation protection and allows for a proper, clear, and concise review of the different classifications in terms of principles of protection, dose criteria, categories, fundamental tools, exposure situations, applications and control measures. With the groundwork laid, advances of the linear no-threshold (LNT) model which has attracted attention in the field of radiobiology and epidemiology were examined in detail. Various plausible dose-response relationship scenarios were x-rayed under low-dose extrapolation. Intensive review of factors opposing the LNT model involving radiophobia (including misdiagnosis, alternative surgery/imaging, suppression of ionizing radiation (IR) research); radiobiology (including DNA damage repair, apoptosis/necrosis, senescence protection) and cost issues (including-high operating cost of LNT, incorrect prioritization, exaggeration of LNT impact, risk-to-benefit analysis) were performed. On the other hand, factors supporting the use of LNT were equally examined, they include regulatory bodies' endorsement, insufficient statistical significance, partial DNA repair, variability of irradiated bodies, different latency periods for cancer, dynamic nature of threshold and conflicting interests. After considering the gaps in the scientific investigations that either support or counter the scientific paradigm on the use of LNT model, further research and advocacy is recommended that will ultimately lead to the acceptance of an alternative paradigm by the international regulators.

[Low back pain and abdominal aortic aneurysm: Red flags]

Approximately 70% of adults will experience low back pain at some point in their life. Most of the cases cannot be identified a cause, being nonspecific pains. The clinical guidelines on the management of low back pain indicate suspecting the presence of serious processes by means of the so-called red flags. Abdominal aortic aneurysm in 91% of cases is accompanied by low back pain, hence its importance of including it as a differential diagnosis. We present the case of a 75-year-old man with low back pain, without improvement with conservative treatment, referred to a rehabilitation consultation 3months after the onset of symptoms, and in the event of warning signs, imaging studies are requested that show abdominal aortic aneurysm and mass right kidney. We must bear in mind the red flags in patients with low back pain, and thus avoid outcomes that can put their lives at risk.

Radiation protection among health care workers: knowledge, attitude, practice, and clinical recommendations: a systematic review

OBJECTIVES

This study was performed to determine the knowledge, attitude, and practice (KAP) of health care workers (HCWs) towards radiation protection.

METHODS

In this systematic review study, three international databases (Web of Science, PubMed, Scopus) were searched for related published articles in the English language from 1 January 2000 to 1 February 2020. The quality of the included studies was evaluated using the Hoy et al. tool.

RESULTS

Out of the 1,848 studies examined, 41 studies that were performed on 11,050 HCWs were included in the final stage. The results indicated that in most studies, more than half (50%) of the participants had average knowledge. Furthermore, 60% of the participants had a positive attitude, but in most studies, they had average practice regarding radiation protection. The most important recommendation for improving KAP among the participants was incorporating radiation protection standards in the student curriculum.

CONCLUSION

Considering the results of the study, further attention should be paid to proper education regarding radiation protection standards and improvement of HCW performance.

Relationship of aortic bifurcation with sacropelvic anatomy: Application to anterior lumbar interbody fusion

INTRODUCTION

Various sacropelvic parameters such as the pelvic Incidence (PI) are used to predict ideal lumbar lordosis and aid surgical planning. The objective of this study was to establish the relationship between the location of the aortic bifurcation from the sacral promontory and sacropelvic measures including the PI.

MATERIALS AND METHODS

One hundred sixty five computed tomography (CT) scans obtained for major trauma including the entire spine were identified. Sacropelvic parameters including PI, sacral anatomic orientation, pelvic thickness (PTH), and sacral table angle were measured. Aortic bifurcation was identified on sagittal and coronal imaging and the distance from the sacral promontory (bifurcation-promontory distance [BPD]) measured (mm).

RESULTS

Mean age of the cohort was 44.3 years (SD 18.5; range 16-88 years); 61.8% male. The mean PI was 49.2° (SD 10.2°; range 30°-80°). The mean BPD was 66.4 mm (SD 13.1 mm; range 38.3-100 mm). In the majority, the bifurcation was at the level of the L4 vertebral body (72.7%). Only age (r = -.389; p < .0001) and PTH (r = .172; p = .027) correlated with the BPD to a significant degree. PI did not correlate with BPD (r = .061; p = .435). Linear regression analysis provided the following predictive equation: BPD = 34.3 mm + 0.30 × PTH.

CONCLUSION

This study demonstrates a lack of any meaningful correlation between sagittal pelvic parameters and the distance of the aortic bifurcation from the sacral promontory. Surgical planning for fusion surgery in the lumbar spine should include assessment of spinopelvic parameters and if anterior access to the lumbar disc(s) necessary, vascular anatomy should be carefully assessed independent of these measures.