Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Two-photon endomicroscopy (2PEM), an endomicroscopic imaging technique based on the two-photon excitation effect, provides several technical benefits, including high spatiotemporal resolution, label-free structural and metabolic imaging, and optical sectioning. These characteristics make it extremely promising for biomedical imaging applications. This paper classifies distal-scanning 2PEMs based on their actuation mechanism (PZT or MEMS) and excitation–collection optical path configuration (common or separate path). Recent representative advancements are reviewed. Furthermore, we introduce its biomedical applications in tissue, organ, and brain imaging with free-behaving mice. Finally, future development directions for distal-scanning 2PEM are discussed....
Background/Objectives: The objective of this study was to clinically validate the performance of the Nanox.AI HealthOST software in detecting incidental vertebral compression fractures (VCFs) on outpatient chest and abdomen CT scans using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A secondary aim was to assess the rate of missed VCFs using initial radiologist reports. Methods: A retrospective analysis was performed on 590 outpatient CT scans. HealthOST, an artificial intelligence solution from Nanox.AI that allows for automated spine analysis using CT images was evaluated against a consensus ground truth established by two radiologists, including a senior musculoskeletal radiologist. Two vertebral body height reduction thresholds were tested: mild (>20%) and moderate (>25%). Original radiologist reports were reviewed to identify missed VCFs. Results: At the 20% threshold, the AI achieved a sensitivity of 92.0%, a specificity of 52.7%, a PPV of 16.5%, and an NPV of 98.5%. At the 25% threshold, sensitivity decreased to 78.0%, while specificity improved to 94.2%, with a PPV of 51.1% and an NPV of 98.2%. The AI identified 88% and 92% of fractures missed by radiologists at the 20% and 25% thresholds, respectively. Conclusions: The Nanox HealthOST AI solution demonstrates potential as an effective screening tool, with threshold selection adaptable to clinical needs with a secondary review by a radiologist that is advisable to ensure diagnostic accuracy. The study further indicates that radiologists often overlook VCFs in reporting non-indicated cases and that AI has a role in enhancing the detection and reporting of vertebral compression fractures in routine clinical practice....
A non-uniform antenna array is proposed to enhance the accuracy of medical microwave imaging systems by increasing the amount of useful information captured about the imaged domain without increasing the number of antennas. These systems have so far been using uniform antenna arrays, which lead to highly correlated signals, limiting the amount of imaging information and adversely affecting diagnostic accuracy. In the proposed non-uniform antenna array method, the optimal number and positions of antennas are calculated with the aim of enhancing spatial diversity and reducing information redundancy. The mutual information coefficient is used as a metric to evaluate and minimize redundancy between received signals. A microwave head imaging system is used to verify the proposed approach. The results of the investigated scenarios show that using a non-uniform antenna configuration outperforms a uniform setup in imaging accuracy and clarity, when using the same number of antennas. Moreover, the reconstructed images demonstrate that using an optimized non-uniform antenna array with fewer elements can outperform a uniform array with more elements in terms of localization accuracy and image quality. The proposed approach improves imaging performance and reduces system complexity, cost, and power consumption, making it a practical solution for real-world biomedical imaging applications....
Background: Traumatic brain injury (TBI) is a major cause of morbimortality in the world, and it can cause potential intracranial hemorrhage (ICH), a life-threatening condition that requires rapid diagnosis with computed tomography (CT). Artificial intelligence tools for ICH detection are now commercially available. Objectives: Investigate the real-world performance of qER.ai, an artificial intelligence-based CT hemorrhage detection tool, in a post-traumatic population. Methods: Retrospective monocentric observational study of a dataset of consecutively acquired head CT scans at the emergency radiology unit to explore brain trauma. AI performance was compared to ground truth determined by expert consensus. A subset of night shift cases with the radiological report of a junior resident was compared to the AI results and ground truth. Results: A total of 682 head CT scans were analyzed. AI demonstrated a sensitivity of 88.8% and a specificity of 92.1% overall, with a positive predictive value of 65.4% and a negative predictive value of 98%. AI’s performance was comparable to that of junior residents in detecting ICH, with the latter showing a sensitivity of 85.7% and a high specificity of 99.3%. Interestingly, the AI detected two out of three ICH cases missed by the junior residents. When AI assistance was integrated, the combined sensitivity improved to 95.2%, and the overall accuracy reached 98.8%. Conclusions: This study shows better performance from AI and radiologist residents working together than each one alone. These results are encouraging for rethinking the radiological workflow and the future of triage of this large population of brain traumatized patients in the emergency unit....
Background: Recently, shear wave elastography (SWE) has been recognized as an effective tool for evaluating Sjögren’s syndrome (SS) patients. The purpose of this study was to assess the parotid glands with SWE, especially for quantitative analysis of shear elastic modulus in relation to age, gender, and internal architecture in patients with oral cancer to collect control data for SS. Methods: In total, 124 parotid glands of 62 patients with oral cancer were evaluated with SWE. The parotid glands were examined for the internal architecture (homogeneous or heterogeneous) on B-mode. The SWE allowed the operator to place regions of interest (ROIs) for parotid glands, and displayed automatically shear elastic modulus data (kPa) for each ROI. Gender and internal architecture were compared with the shear elastic modulus of the parotid glands by Mann–Whitney U-test. The comparison of age and shear elastic modulus was assessed using Spearman’s correlation coefficient. p < 0.05 was considered statistically significant. Results: The shear elastic modulus of the parotid glands was not significantly different for according to gender (males, 7.70 ± 2.22 kPa and females, 7.67 ± 2.41 kPa, p = 0.973) or internal architecture (homogeneous: 7.69 ± 2.25 kPa and heterogeneous: 7.72 ± 2.74 kPa, p = 0.981). Furthermore, the shear elastic modulus was not correlated with age (n = 124, R = −0.133, p = 0.139). Conclusion: Our study showed the control data of the shear elastic modulus of the parotid glands for SS. SWE is useful for the quantitative evaluation of the parotid glands....
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