Modalities for teaching responsible and ethical conduct of research online: Lessons learned from an undergraduate workshop in Utah.

The COVID-19 pandemic disrupted scientific research, teaching, and learning in higher education and forced many institutions to explore new modalities in response to the abrupt shift to remote learning. Accordingly, many colleges and universities struggled to provide the training, technology, and best practices to support faculty and students, especially those at historically disadvantaged and underrepresented institutions. In this study we investigate different remote learning modalities to improve and enhance research education training for faculty and students. We specifically focus on Responsible and Ethical Conduct of Research (RECR) and research mentoring content to help address the newly established requirements of the National Science Foundation for investigators. To address this need we conducted a workshop to determine the effectiveness of three common research education modalities: Live Lecture, Podcast, and Reading. The Live Lecture sessions provided the most evidence of learning based on the comparison between pre- and post-test results, whereas the Podcast format was well received but produced a slight (and non-significant) decline in scores between the pre- and post-tests. The Reading format showed no significant improvement in learning. The results of our workshop illuminate the effectiveness and obstacles associated with various remote learning modalities, enabling us to pinpoint areas that require additional refinement and effort, including the addition of interactive media in Reading materials.

Pedicle Screw Pseudofracture on Computed Tomography Secondary to Metal Artifact Reduction.

Metal artifact reduction (MAR) algorithms are commonly used in computed tomography (CT) scans where metal implants are involved. However, MAR algorithms also have the potential to create new artifacts in reconstructed images. We present a case of a screw pseudofracture due to MAR on CT.

Trends in surgical management of spinal metastases in a Singaporean tertiary referral center: a 17-year retrospective review.

Introduction: Surgical treatment is increasingly the treatment of choice in cancer patients with epidural spinal cord compression and spinal instability. There has also been an evolution in surgical treatment with the advent of minimally invasive surgical (MIS) techniques and separation surgery. This paper aims to investigate the changes in epidemiology, surgical technique, outcomes and complications in the last 17 years in a tertiary referral center in Singapore. Methods: This is a retrospective study of 383 patients with surgically treated spinal metastases treated between January 2005 to January 2022. Patients were divided into 3 groups, patients treated between 2005 - 2010, 2011-2016, and 2017- 2021. Demographic, oncological, surgical, patient outcome and survival data were collected. Statistical analysis with univariate analysis was performed to compare the groups. Results: There was an increase in surgical treatment (87 vs 105 vs 191). Lung, Breast and prostate cancer were the most common tumor types respectively. There was a significant increase in MIS(p<0.001) and Separation surgery (p<0.001). There was also a significant decrease in mean blood loss (1061ml vs 664 ml vs 594ml) (p<0.001) and total transfusion (562ml vs 349ml vs 239ml) (p<0.001). Group 3 patients were more likely to have improved or normal neurology (p=<0.001) and independent ambulatory status(p=0.012). There was no significant change in overall survival. Conclusion: There has been a significant change in our surgical practice with decreased blood loss, transfusion and improved neurological and functional outcomes. Patients should be managed in a multidisciplinary manner and surgical treatment should be recommended when indicated.

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.

Platelet Receptor Glycoprotein VI-Dimer Is Overexpressed in Patients with Atrial Fibrillation at High Risk of Ischemic Stroke.

Introduction Atrial fibrillation (AF) increases the risk of ischemic stroke (IS). We hypothesized that the functional form of platelet receptor glycoprotein (GP) VI, GPVI-dimer, which binds to collagen and fibrin causing platelet activation, is overexpressed in patients with AF who have not had a stroke. Methods A total of 75 inpatients with AF were recruited. None were admitted with or had previously had thrombotic events, including IS or myocardial infarction. Platelet surface expression of total GPVI, GPVI-dimer, and the platelet activation marker P-selectin were quantitated by whole blood flow cytometry. Serum biomarkers were collected in AF patients. Results were compared against patients contemporaneously admitted to hospital with similar age and vascular risk-factor profiles without AF (noAF, n = 30). Results Patients with AF have similar total GPVI surface expression ( p = 0.58) and P-selectin exposure ( p = 0.73) on their platelets compared with noAF patients but demonstrate significantly higher GPVI-dimer expression ( p = 0.02 ). Patients with paroxysmal AF express similar GPVI-dimer levels compared with permanent AF and GPVI-dimer levels were not different between anticoagulated groups. Serum N-terminal pro b-type natriuretic peptide ( p < 0.0001 ) and high sensitivity C-reactive protein ( p < 0.0001 ) were significantly correlated with GPVI-dimer expression in AF platelets. AF was the only vascular risk factor that was independently associated with higher GPVI-dimer expression in the whole population ( p = 0.02 ) . Conclusion GPVI inhibition is being explored in clinical trials as a novel target for IS treatment. As GPVI-dimer is elevated in AF patients' platelets, the exploration of targeted GPVI-dimer inhibition for stroke prevention in patients at high risk of IS due to AF is supported.

Introduction of Androgen Receptor Targeting shRNA Inhibits Tumor Growth in Patient-Derived Prostate Cancer Xenografts.

Patient-derived xenograft (PDX) models have been established as important preclinical cancer models, overcoming some of the limitations associated with the use of cancer cell lines. The utility of prostate cancer PDX models has been limited by an inability to genetically manipulate them in vivo and difficulties sustaining PDX-derived cancer cells in culture. Viable, short-term propagation of PDX models would allow in vitro transfection with traceable reporters or manipulation of gene expression relevant to different studies within the prostate cancer field. Here, we report an organoid culture system that supports the growth of prostate cancer PDX cells in vitro and permits genetic manipulation, substantially increasing the scope to use PDXs to study the pathobiology of prostate cancer and define potential therapeutic targets. We have established a short-term PDX-derived in vitro cell culture system which enables genetic manipulation of prostate cancer PDXs LuCaP35 and BM18. Genetically manipulated cells could be re-established as viable xenografts when re-implanted subcutaneously in immunocompromised mice and were able to be serially passaged. Tumor growth of the androgen-dependent LuCaP35 PDX was significantly inhibited following depletion of the androgen receptor (AR) in vivo. Taken together, this system provides a method to generate novel preclinical models to assess the impact of controlled genetic perturbations and allows for targeting specific genes of interest in the complex biological setting of solid tumors.

Rethinking leadership approaches for community-wide opioid crisis intervention: harnessing positive inquiry to unearth front-line insight.

BACKGROUND/AIM: The opioid crisis presents a complex and widespread health challenge for communities, necessitating a deeper exploration beyond simple solutions. To address this crisis, it is crucial to identify and disseminate best practices. In this study, we focused on positive deviance narratives from Columbus, Ohio, a community deeply affected by the opioid crisis. METHODS: Employing an appreciative inquiry framework, we conducted purposive sampling to interview 47 frontline professionals and individuals grappling with opioid use disorder (OUD). RESULTS: Our research revealed several key insights, highlighting successful themes through positive narratives. First, perceiving OUD as a chronic disease emerged as a critical perspective, acknowledging the long-term nature of the condition and the need for sustained care. Second, re-humanising and de-stigmatising opioid users played a vital role in facilitating their recovery and reintegration into society. Embracing inclusive care delivery was another important aspect, ensuring that individuals from diverse backgrounds receive equitable access to effective treatment. Moreover, re-energising professionals to combat burnout proved essential, emphasising the importance of supporting and motivating healthcare providers in their efforts. Finally, fostering cross-institutional relationship building and collaboration emerged as a significant factor, as it encouraged a coordinated approach to addressing the crisis. Designing adaptive organisational structures also played a crucial role, enabling healthcare institutions to respond effectively to evolving challenges. CONCLUSIONS: Using a positive deviance approach to a challenging public health crisis like OUD can help us discover new and innovative care management approaches for community wide interventions.

Outcomes and Complications of Surgery for Symptomatic Spinal Metastases; a Comparison Between Patients Aged ≥ 70 and <70.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: Physicians may be deterred from operating on elderly patients due to fears of poorer outcomes and complications. We aimed to compare the outcomes of surgical treatment of spinal metastases patients aged ≥70-yrs and <70-yrs. MATERIALS AND METHODS: This is a retrospective study of patients surgically treated for metastatic epidural spinal cord compression and spinal instability between January-2005 to December-2021. Follow-up was till death or minimum 1-year post-surgery. Outcomes included post-operative neurological status, ambulatory status, medical and surgical complications. Two Sample t-test/Mann Whitney U test were used for numerical variables and Pearson Chi-Squared or Fishers Exact test for categorical variables. Survival was presented with a Kaplan-Meier curve. P < .05 was significant. RESULTS: We identified 412 patients of which 29 (7.1%) patients were excluded due to loss to follow-up and previous surgical treatment. 79 (20.6%) were ≥70-yrs. Age ≥70-yrs patients had poorer ECOG scores (P = .0017) and Charlson Comorbidity Index (P < .001). No significant difference in modified Tokuhashi score (P = .393) was observed with significantly more ≥ prostate (P < .001) and liver (P = .029) cancer in ≥70-yrs. Improved or maintained normal neurological function (P = .934), independent ambulatory status (P = .171), and survival at 6 months (P = .119) and 12 months (P = .659) was not significantly different between both groups. Medical (P = .528) or surgical (P = .466) complication rates and readmission rates (P = .800) were similar. CONCLUSION: ≥70-yrs patients have comparable outcomes to <70-yr old patients with no significant increase in complication rates. Age should not be a determining factor in deciding surgical management of spinal metastases.

Estimating the prevalence of diagnosed Alzheimer disease in England across deprivation groups using electronic health records: a clinical practice research datalink study.

OBJECTIVE: Estimate the prevalence of diagnosed Alzheimer's disease (AD) and early Alzheimer's disease (eAD) overall and stratified by age, sex and deprivation and combinations thereof in England on 1 January 2020. DESIGN: Cross-sectional. SETTING: Primary care electronic health record data, the Clinical Practice Research database linked with secondary care data, Hospital Episode Statistics (HES) and patient-level deprivation data, Index of Multiple Deprivation (IMD). OUTCOME MEASURES: The prevalence per 100 000 of the population and corresponding 95% CIs for both diagnosed AD and eAD overall and stratified by covariates. Sensitivity analyses were conducted to assess the sensitivity of the population definition and look-back period. RESULTS: There were 448 797 patients identified in the Clinical Practice Research Datalink that satisfied the study inclusion criteria and were eligible for HES and IMD linkage. For the main analysis of AD and eAD, 379 763 patients are eligible for inclusion in the denominator. This resulted in an estimated prevalence of diagnosed AD of 378.39 (95% CI, 359.36 to 398.44) per 100 000 and eAD of 292.81 (95% CI, 276.12 to 310.52) per 100 000. Prevalence estimates across main and sensitivity analyses for the entire AD study population were found to vary widely with estimates ranging from 137.48 (95% CI, 127.05 to 148.76) to 796.55 (95% CI, 768.77 to 825.33). There was significant variation in prevalence of diagnosed eAD when assessing the sensitivity with the look-back periods, as low as 120.54 (95% CI, 110.80 to 131.14) per 100 000, and as high as 519.01 (95% CI, 496.64 to 542.37) per 100 000. CONCLUSIONS: The study found relatively consistent patterns of prevalence across both AD and eAD populations. Generally, the prevalence of diagnosed AD increased with age and increased with deprivation for each age category. Women had a higher prevalence than men. More granular levels of stratification reduced patient numbers and increased the uncertainty of point prevalence estimates. Despite this, the study found a relationship between deprivation and prevalence of AD.

Epithelioid Sarcoma of the Spine: A Review of Literature and Case Report.

Epithelioid sarcoma is a rare malignant mesenchymal tumor that represents less than 1% of soft-tissue sarcomas. Despite its slow growth, the overall prognosis is poor with a high rate of local recurrence, lymph-node spread, and hematogenous metastasis. Primary epithelioid sarcoma arising from the spine is extremely rare, with limited data in the literature. We review the existing literature regarding spinal epithelioid sarcoma and report a case of epithelioid sarcoma arising from the spinal cord. A 54 year old male presented with a 1-month history of progressive left upper-limb weakness and numbness. Magnetic resonance imaging (MRI) of the spine showed an enhancing intramedullary mass at the level of T1 also involving the left T1 nerve root. Systemic radiological examination revealed no other lesion at presentation. Surgical excision of the mass was performed, and histology was consistent with epithelioid sarcoma of the spine. Despite adjuvant radiotherapy, there was aggressive local recurrence and development of intracranial metastatic spread. The patient died of the disease within 5 months from presentation. To the best of our knowledge, spinal epithelioid sarcoma arising from the spinal cord has not yet been reported. We review the challenges in diagnosis, surgical treatment, and oncologic outcome of this case.

A signature of platelet reactivity in CBC scattergrams reveals genetic predictors of thrombotic disease risk.

Genetic studies of platelet reactivity (PR) phenotypes may identify novel antiplatelet drug targets. However, such studies have been limited by small sample sizes (n < 5000) because of the complexity of measuring PR. We trained a model to predict PR from complete blood count (CBC) scattergrams. A genome-wide association study of this phenotype in 29 806 blood donors identified 21 distinct associations implicating 20 genes, of which 6 have been identified previously. The effect size estimates were significantly correlated with estimates from a study of flow cytometry-measured PR and a study of a phenotype of in vitro thrombus formation. A genetic score of PR built from the 21 variants was associated with the incidence rates of myocardial infarction and pulmonary embolism. Mendelian randomization analyses showed that PR was causally associated with the risks of coronary artery disease, stroke, and venous thromboembolism. Our approach provides a blueprint for using phenotype imputation to study the determinants of hard-to-measure but biologically important hematological traits.

Distress, Social Support, and Self-Compassion: Relationships With Mental Health Among College Students.

This study investigated how distress, conceptualized as an interaction between hassles and stress perceptions, related to mental health, whether the type of distress (social or nonsocial) was consequential, and whether perceived support and self-compassion attenuated these relationships. Students (N = 185) from a mid-sized university in the southeast completed a survey. Survey questions pertained to hassles and stress perceptions, mental health (i.e., anxiety, depression, happiness, and love of life), perceived social support, and self-compassion. As predicted, students reporting more hassles and stress (both social and nonsocial) as well as those reporting less support and self-compassion, were worse off regarding mental illness and mental wellness. This was observed for both social and nonsocial distress. Although we did not support our hypotheses regarding buffering effects, we found perceived support and self-compassion are beneficial, regardless of hassles and stress levels. We discuss implications for students' mental health and ideas for future research.

Multiple Myeloma: Case of a "Moving" Sternal Wire.

Multiple myeloma generally occurs in older adults, with the clonal proliferation of plasma cells and accumulation of monoclonal protein resulting in a broad range of clinical manifestations and complications, including hypercalcemia, renal dysfunction, anaemia, and bone destruction (termed CRAB features). A 64-year-old man with no history of malignancy presented with an enlarging precordial lump occurring three years post-sternotomy for uneventful coronary artery bypass grafting surgery. Initial investigations showed anaemia and impaired renal function. Multimodal imaging performed for further evaluation showcases the radio-pathological features which can be encountered in haematological malignancy. Subsequent percutaneous biopsy confirmed an underlying plasma cell neoplasm, and a diagnosis of multiple myeloma was achieved. The prompt resolution of the lesions upon the initiation of treatment highlights the importance of early diagnosis and treatment.

Novel 3D printable PEEK-HA-Mg2SiO4 composite material for spine implants: biocompatibility and imaging compatibility assessments.

PURPOSE: To develop a novel 3D printable polyether ether ketone (PEEK)-hydroxyapatite (HA)-magnesium orthosilicate (Mg2SiO4) composite material with enhanced properties for potential use in tumour, osteoporosis and other spinal conditions. We aim to evaluate biocompatibility and imaging compatibility of the material. METHODS: Materials were prepared in three different compositions, namely composite A: 75 weight % PEEK, 20 weight % HA, 5 weight % Mg2SiO4; composite B: 70 weight% PEEK, 25 weight % HA, 5 weight % Mg2SiO4; and composite C: 65 weight % PEEK, 30 weight % HA, 5 weight % Mg2SiO4. The materials were processed to obtain 3D printable filament. Biomechanical properties were analysed as per ASTM standards and biocompatibility of the novel material was evaluated using indirect and direct cell cytotoxicity tests. Cell viability of the novel material was compared to PEEK and PEEK-HA materials. The novel material was used to 3D print a standard spine cage. Furthermore, the CT and MR imaging compatibility of the novel material cage vs PEEK and PEEK-HA cages were evaluated using a phantom setup. RESULTS: Composite A resulted in optimal material processing to obtain a 3D printable filament, while composite B and C resulted in non-optimal processing. Composite A enhanced cell viability up to ~ 20% compared to PEEK and PEEK-HA materials. Composite A cage generated minimal/no artefacts on CT and MR imaging and the images were comparable to that of PEEK and PEEK-HA cages. CONCLUSION: Composite A demonstrated superior bioactivity vs PEEK and PEEK-HA materials and comparable imaging compatibility vs PEEK and PEEK-HA. Therefore, our material displays an excellent potential to manufacture spine implants with enhanced mechanical and bioactive property.

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.

Argentination: A Silver Bullet for Cannabinoid Separation by Differential Mobility Spectrometry.

As the legality of cannabis continues to evolve globally, there is a growing demand for methods that can accurately quantitate cannabinoids found in commercial products. However, the isobaric nature of many cannabinoids, along with variations in extraction methods and product formulations, makes cannabinoid quantitation by mass spectrometry (MS) challenging. Here, we demonstrate that differential mobility spectrometry (DMS) and tandem-MS can distinguish a set of seven cannabinoids, five of which are isobaric: Δ9-tetrahydrocannabinol (Δ9-THC), Δ8-THC, exo-THC, cannabidiol, cannabichromene, cannabinol, and cannabigerol. Analytes were detected as argentinated species ([M + Ag]+), which, when subjected to collision-induced dissociation, led to the unexpected discovery that argentination promotes distinct fragmentation patterns for each cannabinoid. The unique fragment ions formed were rationalized by discerning fragmentation mechanisms that follow each cannabinoid's MS3 behavior. The differing fragmentation behaviors between species suggest that argentination can distinguish cannabinoids by tandem-MS, although not quantitatively as some cannabinoids produce small amounts of a fragment ion that is isobaric with the major fragment generated by another cannabinoid. By adding DMS to the tandem-MS workflow, it becomes possible to resolve each cannabinoid in a pure N2 environment by deconvoluting the contribution of each cannabinoid to a specific fragmentation channel. To this end, we used DMS in conjunction with a multiple reaction monitoring workflow to assess cannabinoid levels in two cannabis extracts. Our methodology exhibited excellent accuracy, limits of detection (10-20 ppb depending on the cannabinoid), and linearity during quantitation by standard addition (R2 > 0.99).

Incidental findings involving the temporomandibular joint on computed tomography and magnetic resonance imaging.

The temporomandibular joint (TMJ) is frequently imaged in head and neck computed tomography (CT) and magnetic resonance imaging (MRI) studies. Depending on the indication for the study, an abnormality of the TMJ may be an incidental finding. These findings encompass both intra- and extra-articular disorders. They may also be related to local, regional or systemic conditions. Familiarity with these findings along with pertinent clinical information helps narrow the list of differential diagnoses. While definitive diagnosis may not be immediately apparent, a systematic approach contributes to improved discussions between clinicians and radiologists and better patient management.

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.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44-0.99, 0.63-1.00, and 0.73-0.96, respectively, with AUCs of 0.73-0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.