Three-dimensional (3D) printing plays an important role in cardiovascular disease through the use of personalised models that replicate the normal anatomy and its pathology with high accuracy and reliability. While 3D printed heart and vascular models have been shown to improve medical education, preoperative planning and simulation of cardiac procedures, as well as to enhance communication with patients, 3D bioprinting represents a potential advancement of 3D printing technology by allowing the printing of cellular or biological components, functional tissues and organs that can be used in a variety of applications in cardiovascular disease. Recent advances in bioprinting technology have shown the ability to support vascularisation of large-scale constructs with enhanced biocompatibility and structural stability, thus creating opportunities to replace damaged tissues or organs. In this review, we provide an overview of the use of 3D bioprinting in cardiovascular disease with a focus on technologies and applications in cardiac tissues, vascular constructs and grafts, heart valves and myocardium. Limitations and future research directions are highlighted.

Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor–patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.

Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed using the Ofeye 1.0 tool. These images were then reviewed by a consultant radiologist with findings serving as the reference standard for determining the diagnostic performance of the AI tool for the detection of OVFs. Of all the original radiologist reports, missed OVFs were found in 28.8% of images but were detected using the AI tool. The AI tool demonstrated high specificity of 92.8% (95% CI: 89.6, 95.2%), moderate accuracy of 80.3% (95% CI: 76.3, 80.4%), positive predictive value (PPV) of 73.7% (95% CI: 65.2, 80.8%), and negative predictive value (NPV) of 81.5% (95% CI: 79, 83.8%), but low sensitivity of 49% (95% CI: 40.7, 57.3%). The AI tool showed improved sensitivity compared with the original radiologist reports, which was 20.8% (95% CI: 14.5, 28.4). The new AI tool can be used as a complementary tool in routine diagnostic reports for the reduction in missed OVFs in elderly women.

This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient’s chest CT images, was fabricated using a variety of materials, including polylactic acid (PLA), Glow-PLA, acrylonitrile butadiene styrene (ABS), and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kilovoltage peak (kVp) and milliampere-seconds (mAs). Subjective image quality of each scan (656 images) was evaluated by three radiologists using a five-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Anatomical conformance was assessed by comparing tissue diameters of the phantom and patient scans using Bland–Altman analysis. The phantom’s lung tissue, lung nodules, and diaphragm demonstrated radiation attenuation comparable to patient tissue, as measured in Hounsfield Units (HU). However, significant variations in HU were observed for the skin, subcutaneous fat, muscle, bone, heart, lung vessels, and blood vessels compared to patient tissues, with values ranging from 93.9 HU to −196 HU (p < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.927 and r = 0.931, respectively, p < 0.001, Jamovi, version 2.3.28). The median subjective image quality score from radiologists was 4, indicating good diagnostic confidence across all CT protocols (κ = −0.398, 95% CI [−0.644 to −0.152], p < 0.002, SPSS Statistics, version 30). An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. The measurement error between patient and phantom scans was −0.03 ± 0.14 cm. These findings highlight the potential for significant dose reductions in lung cancer screening programs. Further studies are recommended to improve the phantom by selecting more tissue-equivalent materials.

Lung cancer screening would benefit from low-dose CT protocols optimized by means of a highly accurate three-dimensional radiation-equivalent thoracic phantom. However, whether three-dimensional (3D)-printed chest phantoms have been used for this purpose is unclear, as is their current scope of application. This systematic review aims to explore the range of applications of 3D-printed thoracic phantoms, along with the techniques, materials, and anatomical structures they replicate. Relevant articles were identified using a systematic search strategy across PubMed and Scopus databases, based on pre-determined selection criteria. In total, 20 articles were eligible and critically analysed, all consisting of phantom experiments. Findings reveal that a diverse range of thoracic organs have been 3D-printed, predominantly via fused-deposition modelling incorporating polylactic acid, however, often representing discreet or limited structures. A comprehensive radiation-equivalent chest phantom that mimics the full gamut of thoracic structures is warranted. Most studies are still in their preliminary testing stages, primarily assessing the feasibility of creating morphologically accurate thoracic structures with radiation equivalence. Few studies have progressed to explore their applications. Notably, most investigations into applications have concentrated on dose reduction and CT protocol optimisation for cardiac purposes, rather than pulmonary applications, despite the inclusion of lung cancer nodules in some phantoms.

This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose CT protocols for lung cancer screening. The chest phantom, which was developed from a selected patient’s chest CT images was fabricated using a variety of materials, including PLA, Glow-PLA, ABS, and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kVp and mAs. Subjective image quality was evaluated by three radiologists using a 5-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The phantom's lung tissue, lung nodules, and diaphragm demonstrated radiation attenuation comparable to patient tissue, as measured in Hounsfield Units (HU). However, significant variations in HU were observed for the skin, subcutaneous fat, muscle, bone, heart, lung vessels, and blood vessels compared to patient tissues, with values ranging from 93.9 HU to -196 HU (p < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.92 and r = 0.93, respectively, p < 0.001). The median subjective image quality score from radiologists was 4, indicating good diagnostic confidence across all CT protocols (κ=-0.398, 95% CI [-0.644 to -0152], p < .002.). An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. These findings highlight the potential for significant dose reductions in lung cancer screening programs. Further studies are recommended to improve the phantom by selecting more tissue-equivalent materials.