Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review.

Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.

A Rare Case of Small Bowel Obstruction in a 15-Year-Old Girl: Internal Hernia Associated with Meckel's Diverticulum.

BACKGROUND Meckel's diverticulum is a congenital remnant of the omphalomesenteric duct and is the most common congenital gastrointestinal malformation. Most patients are asymptomatic, but a rare presentation is with subacute small bowel obstruction (SBO) due to herniation of bowel loops through an internal hernia formed by the Meckel's diverticulum and adjacent mesentery that forms an internal hernia. This report is of a 15-year-old girl presenting as an emergency with vomiting and small bowel obstruction due to an internal hernia associated with Meckel's diverticulum. CASE REPORT We present a case of a 15-year-old girl who presented to the Children's Emergency (CE) department with persistent vomiting and abdominal distension and tenderness. X-rays demonstrated dilated small bowel loops, prompting admission under Pediatric Surgery (PAS). A subsequent computed tomography (CT) scan was performed, which demonstrated multiple dilated small bowel loops, confirming SBO, and a blind-ending "C-shaped" bowel loop at the region of the terminal ileum. A diagnostic laparotomy was performed, which confirmed the presence of a Meckel's diverticulum. The tip of the Meckel's diverticulum was adherent to part of the small bowel mesentery, forming an internal hernia defect through which a loop of proximal ileum had herniated, resulting in SBO. She then underwent a laparoscopy-assisted transumbilical Meckel's diverticulectomy (LATUM). The patient recovered uneventfully and was discharged on the 4th postoperative day. CONCLUSIONS In children presenting with SBO, the possibility of Meckel's diverticulum as an etiology should be considered as a differential diagnosis. Early diagnosis and prompt intervention will improve clinical outcomes and avoid complications.

A case report on rapid development of simultaneous liver and musculature calcifications in severe sepsis.

A 76-year-old Malay female presented with 2 days history of fever and vomiting. She was found to have Escherichia coli and Klebsiella pneumoniae bacteraemia with no clear intra-abdominal cause on the initial computed tomography of the abdomen and pelvis (CTAP). She clinically improved with 2 weeks duration of intravenous meropenem. She subsequently developed septic shock and a repeated CTAP demonstrated increased hepatic parenchymal density with extensive parenchymal calcifications. Curvilinear calcifications were seen in the paraspinal and pelvic musculature.

Neonatal Nucleus Accumbens Microstructure Modulates Individual Susceptibility to Preconception Maternal Stress in Relation to Externalizing Behaviors.

OBJECTIVE: Maternal stress influences in utero brain development and is a modifiable risk factor for offspring psychopathologies. Reward circuitry dysfunction underlies various internalizing and externalizing psychopathologies. This study examined (1) the association between maternal stress and microstructural characteristics of the neonatal nucleus accumbens (NAcc), a major node of the reward circuitry, and (2) whether neonatal NAcc microstructure modulates individual susceptibility to maternal stress in relation to childhood behavioral problems. METHOD: K-means longitudinal cluster analysis was performed to determine trajectories of maternal stress measures (Perceived Stress Scale [PSS], hair cortisol) from preconception to the third trimester. Neonatal NAcc microstructural measures (orientation density index [ODI] and intracellular volume fraction [ICVF]) were compared across trajectories. We then examined the interaction between maternal stress and neonatal NAcc microstructure on child internalizing and externalizing behaviors, assessed between ages 3 and 4 years. RESULTS: Two trajectories of maternal stress magnitude ("low"/"high") were identified for both PSS (n = 287) and hair cortisol (n = 336). Right neonatal NAcc ODI (rNAcc-ODI) was significantly lower in "low" relative to "high" PSS trajectories (n = 77, p = .04). PSS at preconception had the strongest association with rNAcc-ODI (r = 0.293, p = .029). No differences in NAcc microstructure were found between hair cortisol trajectories. A significant interaction between preconception PSS and rNAcc-ODI on externalizing behavior was observed (n = 47, p = .047). CONCLUSION: Our study showed that the preconception period contributes to in utero NAcc development, and that NAcc microstructure modulates individual susceptibility to preconception maternal stress in relation to externalizing problems.

Screen time, brain network development and socio-emotional competence in childhood: moderation of associations by parent-child reading.

BACKGROUND: Screen time in infancy is linked to changes in social-emotional development but the pathway underlying this association remains unknown. We aim to provide mechanistic insights into this association using brain network topology and to examine the potential role of parent-child reading in mitigating the effects of screen time. METHODS: We examined the association of screen time on brain network topology using linear regression analysis and tested if the network topology mediated the association between screen time and later socio-emotional competence. Lastly, we tested if parent-child reading time was a moderator of the link between screen time and brain network topology. RESULTS: Infant screen time was significantly associated with the emotion processing-cognitive control network integration (p = 0.005). This network integration also significantly mediated the association between screen time and both measures of socio-emotional competence (BRIEF-2 Emotion Regulation Index, p = 0.04; SEARS total score, p = 0.04). Parent-child reading time significantly moderated the association between screen time and emotion processing-cognitive control network integration (ß = -0.640, p = 0.005). CONCLUSION: Our study identified emotion processing-cognitive control network integration as a plausible biological pathway linking screen time in infancy and later socio-emotional competence. We also provided novel evidence for the role of parent-child reading in moderating the association between screen time and topological brain restructuring in early childhood.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT.

Introduction: Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment. Methods: Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated. Results: Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001). Conclusion: Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.

PURPOSE: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS: We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS: Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION: A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression.

BACKGROUND: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. METHODS: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. RESULTS: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787-0.945) to 0.947 (95% CI 0.899-0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI -0.098-0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49-96.04) to 98.11 (95% CI 93.35-99.77), compared to 44.34 (95% CI 34.69-54.31) for the reports. CONCLUSION: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT.

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2−7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873−0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858−0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803−0.837) and general radiologist (κ = 0.726, 95% CI 0.706−0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI.

Background: Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral. Purpose: To develop a DL model for automated classification of MESCC on MRI. Materials and Methods: Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet's kappa) and sensitivity/specificity were calculated. Results: Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92-0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94-0.95, p < 0.001) compared to the reference standard. Conclusion: A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Clinical application of "black bone" imaging in paediatric craniofacial disorders.

For decades, CT has been the primary imaging modality for the diagnosis and surveillance of paediatric craniofacial disorders. However, the deleterious effects of ionising radiation in the paediatric population are well established and remain an ongoing concern. This is especially so in the head and neck region, which has relatively poor soft tissue shielding with many radiosensitive organs. The development of "black bone" imaging utilising low flip angles and short echo time (TE) has shown considerable promise in alleviating the use of ionising radiation in many cases of craniofacial disorders. In this review article, we share our experience of utilising "black bone" sequence in children with craniofacial pathologies, ranging from traumatic injuries to craniosynostosis and focal osseous/fibro-osseous lesions such as fibrous dysplasia and Langerhans cell histiocytosis (LCH). A detailed discussion on the technical aspects of "black bone" sequence, including its potential pitfalls and limitations, will also be included.

Repurposing the power injectable peripherally inserted central catheter as a tunnelled, non-cuffed, centrally inserted central venous catheter in oncological patients for short- to mid-term vascular access: A pilot study.

OBJECTIVES: Usual short- to mid-term vascular accesses for oncologic patients include the peripherally inserted central catheter and non-tunnelled centrally inserted central catheters, inserted in the supraclavicular or infraclavicular area. Peripherally inserted central catheters can be restrictive in active patients; supraclavicular non-tunnelled centrally inserted central catheters are not ideal in terms of exit site location and cosmesis, while infraclavicular non-tunnelled centrally inserted central catheters may be associated with puncture-related complications. In this pilot study, we have evaluated the off-label use of peripherally inserted central catheters as a tunnelled supraclavicular centrally inserted central catheter. METHODS: Ten patients were recruited for this prospective study. A non-cuffed, power injectable peripherally inserted central catheter was inserted via a short subcutaneous tunnel into the internal jugular vein using the peel-away sheath and introducer as a tunneller. Puncture wounds were closed with tissue glue. Patients were followed up for comfort scores, dwell time and complications. RESULTS: The median dwell time was 94 days (mean of 113 days). One catheter was removed due to systemic fungemia, resulting in an acceptable complication rate of 0.97 per 1000 catheter days.Mean patient-reported comfort scores was 16 (out of 20). Pressurised injections for computer tomography imaging were performed in five patients without complications. CONCLUSION: Despite limited numbers, this method appears to be safe and well accepted with low complication rates. This modified vascular access is low profile, easily concealed, readily removable and compatible with pressure injector and uses a commonly found catheter in a modified fashion. Larger prospective trials will be needed to ascertain if it can be a standard of care for oncological patients.