Tomography

Objectives: The aim of this study is to provide early diagnosis of osteoporosis, osteopenia, and normal bone mineral density (BMD) on CT scans utilizing machine learning and radiomics techniques. Methods: In this retrospective study, 874 patients who met the predefined eligibility criteria were included. BMD was categorized as normal, osteopenia, or osteoporosis according to DEXA-derived T-scores. Lumbar vertebral bodies were automatically segmented on abdominal and lumbar CT images, and radiomic features were extracted from the segmented regions. Logistic regression (LR), decision table (DT), random forest (RF), multilayer perceptron (MLP), and bagging models were developed to distinguish among the three BMD categories. Model performance was evaluated using accuracy, F-score, area under the receiver operating characteristic curve (ROC area), and area under the precision–recall curve (PRC area). Results: Notably, the best results were observed in the L3 vertebra among the 874 patients included in the study. Accordingly, the MLP and LR methods showed the greatest accuracy value of 83.5%, F-score value of 81.2%, ROC area value of 0.912, and PRC area value of 0.892 in cases with normal BMD. At the L2 vertebral level, the LR approach produced the greatest results for patients with osteoporosis (Acc: 72.5%, F-score: 72.5%, ROC area: 0.897, and PRC area: 0.797). Conclusions: In the three-class BMD assessment, the models showed the highest performance for normal BMD and the lowest performance for osteopenia. Among the individual lumbar levels, L3 demonstrated the highest radiomics-based classification performance, whereas L1 showed comparatively lower performance. These level-specific findings should be regarded as exploratory and should not be interpreted as supporting clinical BMD classification from a single vertebra. CT radiomics may have potential as an opportunistic screening tool to identify patients who may benefit from formal DEXA or further osteoporosis evaluation; however, patient-level multivertebral validation is required before clinical implementation.

Background: Artificial intelligence has revolutionized the field of dentistry. This systematic review evaluated the clinical performance of artificial intelligence (AI)-assisted cephalometric tracing compared with manual or expert digital tracing. Methods: We searched the PubMed/MEDLINE, Scopus, and Web of Science databases from inception to 20 May 2026. Studies directly comparing AI-assisted tracing with manual or expert digital tracing of the same lateral cephalograms and reporting at least one clinically relevant cephalometric outcome were included in the review. Two authors independently screened records. Risk of bias was assessed using an adapted QUADAS-2 framework. A quantitative meta-analysis was planned but not performed. Findings were synthesised narratively by measurement, platform, and workflow, with reporting informed by the Synthesis Without Meta-analysis (SWiM) guideline. Results: Twenty studies were included. Direct AI-versus-reference ICCs were reported in more than three studies, but their formulations and reference procedures varied. Small average differences sometimes coexisted with wide individual limits of agreement. The numerical evidence did not establish a consistent ranking of SNA, SNB, and ANB. Conclusions: AI-assisted tracing may be useful as a clinician-verified adjunct, but its performance depends on the measurement, platform, and workflow. The evidence does not establish uniform clinical interchangeability with manual or expert digital tracing.

Background: Infective endocarditis (IE) remains a life-threatening disease associated with substantial morbidity and mortality. Transthoracic and transesophageal echocardiography are the first-line imaging modalities, but their diagnostic performance may be reduced in patients with prosthetic valves, intracardiac devices, previous cardiac surgery, extensive calcification, or suspected periannular extension. Cardiac computed tomography (CT) provides complementary high-resolution anatomical information and has gained increasing importance within contemporary multimodality diagnostic pathways. Scope: This Review critically examines the current role of cardiac CT in IE, including acquisition and reconstruction strategies, lesion-specific imaging findings, applications in native, prosthetic, transcatheter, and device-related infection, extracardiac assessment, and preoperative planning. Comparative observations regarding echocardiography and selected multimodality techniques are integrated where directly relevant. Emerging applications of spectral CT, photon-counting detector CT, deep learning reconstruction, radiomics, and artificial intelligence are appraised according to four levels of evidence: clinically validated IE applications, preliminary IE-specific evidence, indirect cardiovascular evidence, and conceptual applications. Main Findings: Cardiac CT is particularly valuable for detecting and anatomically characterizing periannular complications, including abscesses, pseudoaneurysms, fistulous tracts, inflammatory extension, and prosthetic dehiscence. Multiphase electrocardiography (ECG)-gated acquisition improves the assessment of dynamic valvular and prosthetic abnormalities. Transesophageal echocardiography remains more sensitive for small or highly mobile vegetations, leaflet perforation, regurgitant jets, and intracardiac flow abnormalities, demonstrating that the two techniques are complementary rather than interchangeable. Cardiac CT also contributes to coronary assessment, surgical planning, evaluation of previous grafts and reconstructive material, and selected extracardiac complications. Conclusions: Cardiac CT is an important component of contemporary multimodality IE assessment, but it should not be regarded as a replacement for echocardiography. Its clinical value depends on the infected substrate, suspected complication, acquisition protocol, and integration with echocardiographic, microbiological, surgical, and clinical findings. Emerging technologies may improve image quality and quantitative characterization, but most remain supported by preliminary IE-specific, indirect cardiovascular, or conceptual evidence and should not yet be considered established IE-specific diagnostic tools.