To systematically review and meta-analyse the prevalence of incidental findings (IFs) requiring interventions identified in CT performed for patients with traumatic injuries in the emergency department, including pathologically confirmed cancers and emergent non-traumatic vascular pathologies.

Systematic review and meta-analysis.

PubMed, EMBASE and Cochrane CENTRAL databases from database inception to 22 November 2024.

Prospective or retrospective studies involving traumatic injury patients presented at the emergency department reporting IFs of clinical significance detected through CT with any interventions proposed were included. Studies that exclusively included paediatric populations were excluded. The systematic review methods included double-screening, dual assessment of eligibility and study validity, dual data extraction, Bayesian multivariate random-effects meta-analysis of prevalence and employing the Grading of Recommendations, Assessment, Development and Evaluations’ rating for the certainty of evidence. The primary outcomes were IFs requiring any interventions, and subset IFs requiring urgent interventions. Secondary outcomes were histologically confirmed cancers and emergent non-traumatic vascular pathologies.

22 studies (1 with a prospective and 21 with a retrospective design) mainly from high-income countries using limited-quality data based on clinical practice involving 18 538 patients were included. 9 studies evaluated the whole body, while 13 evaluated selective body regions. The grading criteria for IFs were non-uniform, and the image interpreters involved had diverse experience and expertise. The summary prevalence estimates for IFs requiring any interventions, urgent interventions, pathologically confirmed cancers and emergent non-traumatic vascular pathologies detected in the whole body were 29.8% (95% credible interval (CrI) 20.4% to 42.9%; very low certainty), 7.6% (95% CrI 4.5% to 14.8%; low certainty), 0.6% (95% CrI 0.3% to 1.6%; moderate certainty) and 0.3% (95% CrI 0.1% to 0.9%; moderate certainty), respectively. These findings were largely identified in the chest or abdomen and pelvis, with the overall detection frequency reduced with the scanned body regions narrowed (very low to moderate certainty). Sparse data on the head, neck and spine resulted in limited results.

IFs identified in trauma whole-body CT requiring intervention are prevalent and can lead to substantial medical costs. The widely reported prevalence range suggests variations in radiologist recommendations and reporting in clinical practice and calls for standardisations. IFs requiring urgent intervention are not rare, which leads to a diagnosis of significant diseases including cancers and urgent vascular pathologies. Future studies should report long-term, patient-relevant results based on standardised classification and reporting systems.

CRD42020187852.

To synthesise the prevalence and patterns of dementia-relevant structural brain MRI abnormalities in adults with suspected or confirmed dementia in low- and middle-income countries (LMICs), and to summarise MRI protocols and the incremental diagnostic contribution of MRI beyond cognitive screening.

Systematic review and meta-analysis.

PubMed, EMBASE, Web of Science and PsycINFO (January 1990–27 January 2025), plus reference list screening and targeted manual searches.

Observational or diagnostic-accuracy studies from World Bank-defined LMICs including adults (≥50 years) with suspected or confirmed dementia who underwent brain MRI as part of diagnostic evaluation.

Two reviewers independently screened, extracted data and assessed risk of bias using ROBINS-I. Random-effects models pooled prevalence of dementia-relevant MRI abnormalities; diagnostic-accuracy outcomes were synthesised narratively due to heterogeneous reference standards and incomplete reporting.

39 LMIC studies were included; 23 studies (2513 participants) contributed to the meta-analysis. Dementia-relevant MRI abnormalities (defined as ≥1 clinically relevant structural abnormality per study definition) were present in 1248/2513 participants. The pooled prevalence of dementia-relevant MRI abnormalities was 58% (95% CI 43% to 72%), with substantial heterogeneity (I²=95%) and a wide prediction interval (8–96%), indicating marked between-study variability; this estimate should be interpreted as a descriptive summary of study-level proportions rather than a precise population parameter.

Brain MRI frequently demonstrates dementia-relevant pathology in LMIC clinical cohorts, usually with mixed neurodegenerative-vascular patterns. Structured visual ratings may add aetiologic specificity beyond cognitive screening, but pooled estimates should be interpreted as summaries of heterogeneous study-level findings rather than precise population parameters, given high heterogeneity and risk of bias.

CRD42024510241.

To compare clinical radiography training experiences (structure, resources, participation, feedback) and self-perceived competence/practice readiness between public and private radiography centres in Lagos State, Nigeria.

Comparative cross-sectional survey design from August to October 2025 using a validated self-administered questionnaire distributed in person during departmental seminars and clinical debriefings at University of Lagos-affiliated centres.

Centre-based settings at public and private radiodiagnostic centres.

A total of 260 final-year students and recent graduates, 130 each from public and private radiodiagnostic centres. Inclusion criteria included: age ≥18 years, with ≥6 months clinical exposure, from centres affiliated to the University of Lagos. All participants completed the self-administered questionnaire. There were no interventions.

The primary outcome was the self-perceived competence/practice readiness, and the secondary was participation, extent and feedback mechanisms, measured as planned without protocol deviations. All variables were measured using validated items in the questionnaire.

Private centres significantly outperformed public centres in hands-on practice and feedback, with higher self-perceived competence (mean 35.6±5.7 vs 32.8±6.4; p=0.001). There were no significant differences in training structure (p=0.78). Public centres reported higher patient loads (86.2% vs 68.5%; p=0.001) but lower equipment availability (47.7% vs 72.3%; p

Private centres were associated with higher self-perceived competence and readiness, better resources and feedback, while public centres offered greater patient volumes. Hybrid placements and targeted infrastructure investment are recommended to help address disparities in perceived readiness.

Incidental pulmonary nodules (IPNs) are commonly encountered on chest radiographs (CXRs) performed for routine clinical indications and may represent early manifestations of significant pulmonary pathology, including lung cancer. While low-dose CT screening has mortality benefits in selected high-risk populations, its implementation remains limited in many healthcare settings. Artificial intelligence (AI)-assisted CXR interpretation has the potential to enhance pulmonary nodule detection. However, evidence from Malaysian clinical practice is scarce. This study aims to evaluate the diagnostic performance of AI-assisted CXR interpretation for detecting IPNs across healthcare facilities in the Klang Valley, Malaysia.

This prospective, multicentre study will include 2452 CXRs from patients aged ≥35 years over a 6-month period across four Klang Valley healthcare facilities. Each CXR will be independently interpreted by an experienced radiologist (>5 years of experience) and analysed separately using an AI system (qXR-LNMS). An independent thoracic radiologist will determine the final classification for analysis if there is IPN detection discordance. Diagnostic performance metrics (sensitivity, specificity, positive and negative predictive values, and overall accuracy) will be calculated using a 2x2 classification matrix. Agreement between AI-assisted interpretation and radiologist reports will be assessed using Cohen’s kappa statistic. The prevalence of IPNs detected by AI-assisted interpretation and radiologist reporting will be compared using a two-proportion z-test. AI discriminative performance will be evaluated using receiver operating characteristic curve analysis and area under the curve estimation. Statistical analyses will be conducted using Statistical Package for the Social Sciences V.29, with p

Ethical approval has been obtained from the Universiti Kebangsaan Malaysia Research Ethics Committee and the Ministry of Health Malaysia Medical Research and Ethics Committee. Written informed consent will be obtained from all participants. The findings will be disseminated through peer-reviewed publications, scientific conferences and engagement with relevant stakeholders.

To describe the structured process of threshold optimisation for a commercially available multiclass chest X-ray (CXR) deep learning model, to evaluate its diagnostic performance across different operating thresholds, and to estimate its potential operational impact within an artificial intelligence (AI)-enabled triage workflow in a primary care setting.

Retrospective diagnostic performance evaluation with threshold-based analysis.

Primary care radiography services in Singapore, using data derived from two primary care clinics and a tertiary hospital.

A total of 816 adult frontal chest radiographs were included (multiethnic Asian, 464 males, 352 females; mean age 60.8 years). Images were selected to represent the spectrum of findings often encountered in primary care. Exclusion criteria included paediatric studies, lateral or oblique radiographs, and findings not supported by the AI model (eg, bony abnormalities and medical devices).

Primary outcome measures were sensitivity, specificity, and negative and positive predictive value (NPV and PPV). Secondary outcomes included estimated potential operational improvement, which is calculated by dividing the number of true negatives by the total number of CXRs.

At the default threshold of 0.15, the AI model achieved a sensitivity of 87.3% (95% CI 83.9% to 90.4%) and an NPV of 87.0% (95% CI 83.6% to 90.2%). Lowering the threshold to 0.10 increased sensitivity to 93.2% (95% CI 90.7% to 95.5%) and NPV to 91.3% (95% CI 88.2% to 94.3%), with specificity of 71.7% (95% CI 67.3% to 76.1%). These trade-offs were considered acceptable for a safety-focused co-triage workflow prioritising minimisation of false negatives.

Threshold optimisation is critical for adapting AI models to context-specific clinical workflows. Our study shows that adjusting the operating threshold enabled prioritisation of sensitivity and NPV, supporting safe AI-assisted triage in primary care. This is a deeply collaborative process that must involve radiology and clinical teams: selecting appropriate thresholds aligned with clinical objectives for safe and effective implementation. Future work will assess real-world operational impact and user acceptance following prospective deployment.

A growing number of national diagnostic reference levels based on clinical indications (NDRL_ci) in CT have been implemented worldwide since the International Commission on Radiological Protection’s 2017 recommendation. This study aims to compare NDRL_ci practices, identify influencing factors and propose evidence-based recommendations for NDRL_ci development, based on the literature published between 1996 and 2025.

Systematic review.

A systematic literature search was conducted in PubMed, Web of Science and Scopus from 1996 to 24 august 2025. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis framework was followed to report the study selection process in this review. Joanna Briggs Institute’s critical appraisal tools were used to evaluate the articles critically.

Adult patients undergoing CT scans for various clinical indications.

Clinical indication-based CT protocols with reported NDRL_ci values as CT dose index volume and dose length product (DLP).

The primary outcomes were NDRL_ci values reported for various clinical indications. The secondary outcomes were CT technology, protocol parameters and patient characteristics influencing NDRL_ci.

A total of 4146 articles were identified. 410 full texts were examined and 11 studies were included in the systematic review. 25 clinical indications across seven anatomical regions were identified across 11 included studies. The NDRL_ci for urinary stones and cerebrovascular accident had the highest number of references, while flank pain and occlusion had the lowest number. The highest NDRL_ci in DLP was found for total body CT in severe trauma (3830 mGy cm) and the lowest for sinusitis (70 mGy cm).

Several factors contribute to dose discrepancies for the same clinical indications in CT imaging, including kilovolt peak and milliampere-second, scan length, number of phases, patient size, reconstruction algorithm, CT scanner age and specifications, underscoring the need for standardised and optimised CT protocols. This review highlighted several challenges, which emphasise the importance of international organisations to standardise the development of NDRL_ci to improve comparability across countries.

CRD42024603574.

To evaluate the incremental diagnostic value and sub-phenotyping capability of Cardiovascular Magnetic Resonance (CMR) compared with Transthoracic Echocardiography (TTE) in patients with elevated left ventricular filling pressure (LVFP).

Prospective registry study. [Results from ClinicalTrials.gov ID NCT05114785]

A single NHS hospital in the UK.

The primary outcome was the rate of diagnostic discordance between TTE and CMR. Secondary outcomes included the characterisation of specific pathologies identified by CMR where TTE was normal, non-diagnostic or provided a non-specific diagnosis.

CMR demonstrated diagnostic discordance with TTE in 74% (n=194) of cases. In patients with a normal TTE (n=54), CMR identified heart failure with preserved ejection fraction (HFpEF) in 46% (n=25) and ischaemic heart disease (IHD) in 19% (n=10). For non-diagnostic TTE cases (n=15), CMR detected HFpEF in 53.3% (n=8) and IHD in 26.7% (n=4). Among those with non-specific left ventricular hypertrophy on TTE (n=47), CMR revealed HFpEF in 45% (n=21) and hypertrophic cardiomyopathy in 34% (n=16).

CMR markedly improves diagnostic precision and sub-phenotyping in patients with elevated LVFP, identifying key conditions like HFpEF, IHD and specific cardiomyopathies that TTE frequently misses. These findings highlight CMR’s critical role as a complementary imaging tool for refining diagnoses and informing management strategies in cardiovascular conditions.

MRI is increasingly recognised as a valuable tool for assessing prognosis and predicting outcomes following traumatic spinal cord injury (SCI). Several potential MRI biomarkers have been identified, but efforts are still needed to improve the accuracy and feasibility of these biomarkers in clinical practice. This study aims to build a national Canadian SCI imaging repository for storing and analysing imaging data for SCI, with the goal of improving SCI MRI biomarkers to predict outcomes and inform clinical management.

As a substudy of the Rick Hansen SCI Registry (RHSCIR), this retrospective multisite study includes individuals who sustained a traumatic cervical SCI between 2015 and 2021, were previously enrolled in RHSCIR, and had MRI scans acquired within 72 hours of injury and before any surgical intervention. Individuals with a penetrating trauma and/or with any prior spine surgery are excluded. The study principal investigator and research associates, experienced with data curation and with the standardised format and specifications of the Brain Imaging Data Structure standard, guide the site’s curator on the steps to perform image deidentification and curation to create standardised datasets across all sites. These datasets are transferred to a Digital Research Alliance of Canada (‘the Alliance’) server designated for this project and concatenated to form the national Canadian SCI imaging repository (Neurogitea). We are using a semiautomated processing pipeline to quantify lesion morphology, together with additional imaging measures that are manually extracted from the images (for instance, the relative maximal spinal cord compression and the maximum canal compromise). Through linkage to RHSCIR clinical and epidemiological data already available on eligible participants, regression analysis is planned to predict neurological outcomes at discharge, including the American Spinal Injury Association Impairment Scale grade, upper and lower extremity motor and sensory scores.

This protocol has been submitted by the participating sites to obtain ethics and institutional approvals prior to the study initiation at each site. All 12 sites across Canada have now obtained ethics and institutional approvals. Study results will be disseminated at local, national and international conferences and by journal publications.

This study aimed to investigate the prevalence of brachydactyly type A3 (BDA3) and its associated epiphysial development abnormalities in Chinese children aged 3–17 years, and to explore differences based on gender, region and urban–rural demographics.

Cross-sectional study.

The study was conducted across 14 provinces (28 survey sites) in China, as part of a nationwide investigation on skeletal maturation. The population was selected using multistage stratified randomised cluster sampling.

A total of 17 850 children (8856 boys and 8994 girls) aged 3–17 years participated. The cohort was drawn from a large-scale survey conducted between 2019 and 2021. Selection criteria included children with no visible clinodactyly or hand function impairments.

Non-dominant hand-wrist radiographs were obtained using a portable X-ray device. A retrospective analysis of these radiographs was performed to identify BDA3 and epiphysial development abnormalities. Prevalence rates were calculated and compared across gender, regional and urban–rural groups.

The overall prevalence of BDA3 was 10.0%, with a higher prevalence in girls (12.9% vs 7.1%; p

This nationwide, multicentre study provides the first national epidemiologic data on BDA3 and associated epiphysial features in Chinese children and adolescents, establishing a prevalence of 10.0%. This baseline supports counselling that a straight, well-functioning short fifth finger is a common anatomic variant and may help reduce unnecessary concern.

The utility of brain MRI in dementia diagnosis offers critical insights into structural brain changes, such as hippocampal and thalamic atrophy, which are hallmark features of Alzheimer’s disease and Alzheimer’s disease-related dementias . However, its use, especially in low- and middle-income countries (LMICs), is affected by limited accessibility. This protocol outlines a systematic review and meta-analysis to assess the diagnostic utility, feasibility and challenges of integrating brain MRI for dementia diagnosis in LMICs.

The review follows Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols with a priori eligibility criteria and is registered in PROSPERO. Searches (from inception to September 2025) will be run in MEDLINE/PubMed, EMBASE, Web of Science and PsycINFO, with supplementary bibliography screening. Adults ≥50 years in LMIC settings undergoing brain MRI for dementia evaluation will be eligible. Data will be synthesised narratively and, where appropriate, via random-effects meta-analysis with planned subgroup analyses by MRI approach (qualitative vs quantitative), magnet strength, sequence availability and reference standard. Screening and data extraction are planned for 1 November to 30 December 2025.

Ethical approval was obtained from the Makerere University School of Medicine Research and Ethics Committee (Mak-SOMREC; Ref Mak-SOMREC-2022-337). For verification, contact the SOMREC Administrator at rresearch9@gmail.com. Departmental contact: Dr Geoffrey Erem, Head, Department of Radiology, Makerere University College of Health Sciences (dreremgeoffrey@gmail.com). Only published data will be used, with no new patient contact. Findings will be disseminated via peer-reviewed publication, conference presentations and policy briefs (and, where feasible, mainstream media) to inform clinical practice and training in LMICs.

CRD42024510241.

Overuse of CT scans is associated with multiple harms, such as an increased risk of cancer development, particularly in children. However, the rate of CT scan use is high and unwarranted worldwide.

This study aimed to identify the patterns and reported indications for head CT scans ordered for non-traumatic paediatric cases in Palestine.

This was a retrospective, cross-sectional study based on a desk review.

The study was carried out from June 2024 to September 2024 in five hospitals located in five major Palestinian governorates in the West Bank.

The study included records of children aged 14 or younger, presenting with non-traumatic complaints and having undergone head CT between January 2020 and September 2024. A total of 3715 patient records were explored, of which 2977 were included in the final analysis; 1764 (59.3%) males and 1213 (40.7%) females.

A pilot review of 100 records was conducted, and the data collection spreadsheet included demographic and clinical characteristics, presentations, reported reasons for CT requests, CT results, and information on lumbar puncture (LP) performance.

The mean age of patients was 4.3 years (SD±3.3), with 59.3% aged 3 to 11 years, and 47.7% presenting to hospitals in northern governorates. The most commonly reported presentation was fever and convulsion (8.2%), followed by convulsions (7.7%), and a combination of fever, headache and vomiting (6.5%). Only 12.9% of the CT scans yielded positive findings, including dilated ventricles (19.3%), sinusitis (18.8%), brain oedema (12.9%), and brain mass (11.1%). Most CT scans were requested to check for contraindications to LP, with only 4.1% having a positive CT finding indicating a contraindication. At the multivariate level, a positive CT result was associated with being a neonate, having a past medical condition, ordering CT to check for contraindication to LP and presenting with convulsions.

CT scans were found to be overused without justification, particularly for ruling out contraindications to LP. The development of clear and specific national guidelines is recommended. This process can be supported through training, decision support tools, alternative management pathways and specialist consultations to ensure compliance. Additionally, enhancing reporting quality and using health information systems are vital for monitoring and improving radiological safety.

The standard treatment for unresectable head and neck cancer typically involves radiotherapy (RT) alone or chemoradiotherapy (chemo-RT). Non-squamous cell carcinomas exhibit relatively low radiosensitivity, limiting the efficacy of conventional photon RT. Carbon-ion (C-ion) RT, characterised by high linear energy transfer (LET) and high relative biological effectiveness (RBE), has shown promising outcomes in treating radioresistant head and neck cancers. However, local recurrences still occur, and further improvements in treatment outcomes are needed. To enhance the local control rate, an increase in dose-averaged LET (LETd) to the tumour was considered.

Following a simulation study, a clinical trial was conducted to optimise LETd using only C-ion therapy, and its safety was confirmed. However, in this clinical trial, LETd could only be increased to approximately 70 keV/μm. To further escalate LETd, multi-ion therapy using ions heavier than carbon was developed. Simulation studies demonstrated that multi-ion therapy incorporating carbon, oxygen and neon ions could increase LETd up to 90 keV/μm, regardless of tumour size, while maintaining high-dose uniformity within the tumour. Based on these results, a clinical study was planned to evaluate the safety of escalating LETd from 70 keV/μm to 90 keV/μm using multi-ion therapy. The primary objective of this study is to evaluate the safety of escalating LETd to the tumour using multi-ion therapy for head and neck cancer, with the secondary goal of identifying the maximum tolerated LETd.

This is a non-randomised, open-label, phase 1 study focused on LETd escalation. A maximum of 18 patients with histologically confirmed inoperable head and neck malignancies will be enrolled. All patients will receive multi-ion therapy using helium, carbon, oxygen or neon ions, either alone or in combination, at an RBE-weighted dose ranging from 57.6 to 70.4 Gy, delivered in 16 fractions (4 fractions per week) over 4 weeks. The specific dose will be determined according to histology. LETd escalation will begin at 70 keV/μm and will increase by 10 keV/μm increments, reaching a maximum of 90 keV/μm. The safety of multi-ion therapy will be assessed based on the frequency and severity of dose-limiting toxicities, monitored up to 90 days after the initial irradiation. Patients will be followed up according to the protocol for 180 days after the initial multi-ion therapy irradiation.

The study protocol has been approved by the National Institutes for Quantum Science and Technology Certified Review Board (#L24-002). The results will be published in a peer-reviewed journal and presented at a scientific conference.

jRCTs032240451.

Progress at the intersection of artificial intelligence and paediatric neuroimaging necessitates large, heterogeneous datasets to generate robust and generalisable models. Retrospective analysis of clinical brain MRI scans offers a promising avenue to augment prospective research datasets, leveraging the extensive repositories of scans routinely acquired by hospital systems in the course of clinical care. Here, we present a systematic protocol for identifying ‘scans with limited imaging pathology’ through machine-assisted manual review of radiology reports.

The protocol employs a standardised grading scheme developed with expert neuroradiologists and implemented by non-clinician graders. Categorising scans based on the presence or absence of significant pathology and image quality concerns facilitates the repurposing of clinical brain MRI data for brain research. Such an approach has the potential to harness vast clinical imaging archives—exemplified by over 250 000 brain MRIs at the Children’s Hospital of Philadelphia—to address demographic biases in research participation, to increase sample size and to improve replicability in neurodevelopmental imaging research. Ultimately, this protocol aims to enable scalable, reliable identification of clinical control brain MRIs, supporting large-scale, generalisable neuroimaging studies of typical brain development and neurogenetic conditions.

Studies using datasets generated from this protocol will be disseminated in peer-reviewed journals and at academic conferences.

The increasing volume of radiological images and the associated workload of report generation necessitate efficient solutions, making artificial intelligence (AI) a crucial tool to streamline this process for radiologists. Recent years have seen a surge in research exploring AI-driven radiological report generation directly from images, particularly with the emergence of large vision language models. However, a comprehensive understanding of the current landscape, including specific limitations and the extent to which efforts move beyond abnormality detection to full textual report generation, remains unclear. This scoping review aims to systematically map the existing literature to provide an overview of the current state of AI in generating radiological reports from medical images, including the scope and limitations of existing research. To our knowledge, no prior scoping review has comprehensively mapped this landscape, especially considering recent advancements in foundation models in medicine and related AI architectures. Considering the explosive growth of related studies in recent years, a comprehensive scoping review will be significant in mapping the current research status and understanding relevant limitations.

This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews guidelines to map the literature on AI generating radiological reports from medical images. We will search PubMed, Scopus and Web of Science for peer-reviewed articles (January 2016 to March 2025) using keywords related to AI, radiological reports and medical images. Original research in English focusing on AI-driven report generation from images will be included and studies without report generation or not using medical images as input will be excluded. Two independent reviewers will perform a two-stage screening. Data extraction, guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and focusing on study characteristics, AI methods, image modalities, report features, limitations and key findings, will be analysed using narrative and descriptive synthesis, with results presented in tables, figures and a narrative summary.

This protocol describes a scoping literature review methodology that does not involve research on humans, animals or their data; therefore, no ethical approval is required. Following the review, the results will be considered for publication in a relevant peer-reviewed journal and may be shared with stakeholders through reports or summaries.