OBJECTIVE

The aim of this study was to qualitatively review the effects of genotoxicity and cytotoxicity on buccal mucosal epithelial cells after cone beam computed tomography (CBCT) exposure focusing on DNA damage and cell changes.

A literature search was carried out in PubMed, Wiley, Google Scholar, and Semantic Scholar for articles published in the last five years. In vivo studies that analyzed the DNA damage and cell changes on buccal mucosal epithelial cells, before and several days after CBCT exposure were included in this review. This review was prepared according to the PRISMA checklist for systematic review and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.

A total of four studies were included in this review. The risk of bias analysis showed that all studies had generally good methodological quality. All the studies used buccal epithelial cells to analyze micronucleus (MN) as a parameter for DNA damage (genotoxicity), three of the studies also analyzed cytotoxicity using pyknotic nucleus and three studies analyzed karyolysis and karyorrhexis. All the studies consistently reported a significant increase in MN frequency, and cytotoxic effect were more evident before and 10-15 days after CBCT exposure.

This study demonstrated a significant impact on DNA and cell damage in oral mucosal cells following CBCT examination. The effect of ionizing radiation from CBCT has a more pronounced impact on cell damage than DNA damage.

OBJECTIVE

To determine the foveal avascular zone (FAZ) thickness and other FAZ measurements in eyes of healthy Filipino adults aged 20-49 years.

This single-center, cross-sectional, observational study evaluated 186 subjects (372 eyes) of Filipino adults 20 to 49 years of age. Using Angioplex®, the automated software of the optical coherence tomography angiography (OCTA) machine, the FAZ area, perimeter, circularity, vascular density, and perfusion in the superficial FAZ were recognized. Statistical analysis was done using SPSS version 23. The mean and standard deviation of the values of superficial capillary free zone measurements were calculated.

The mean FAZ area was 0.297 ± 0.112 mm2, the mean circularity was 0.667 ± 0.080, and the mean perimeter was 2.316 ± 0.80 mm. These parameters were all larger in the female population (p CONCLUSIONS

In eyes of healthy Filipino adults aged 20-49 years, females exhibited larger FAZ area, higher circularity, and lower CMT, compared to males. Additionally, females displayed lower central vessel density and perfusion. While FAZ area and perimeter remained stable with age, circularity decreased, and CMT increased.

INTRODUCTION: Out-of-hospital-cardiac-arrest (OHCA) is a major public health challenge and post-return-of-spontaneous-circulation (ROSC) goals have shifted from just survival to survival with intact neurology. Although post-ROSC care is crucial for survival with intact neurology, there are insufficient well-established protocols for post-resuscitation care. We aimed to evaluate post-resuscitation care in the emergency department (ED) of adult (aged ≥16 years) OHCA patients with sustained ROSC and its associated neurologically intact survival. METHODS: A retrospective review of electronic medical records was conducted for OHCA patients with sustained ROSC at the ED. Data including demographics, pre-hospital resuscitation, ED resuscitation, post-resuscitation care and eventual outcomes were analysed. RESULTS: Among 921 OHCA patients, 85 (9.2%) had sustained ROSC at the ED. Nineteen patients (19/85, 22.4%) survived, with 13 (13/85, 15.3%) having intact neurology at discharge. Electrocardiogram and chest X-ray were performed in all OHCA patients, whereas computed tomography (CT) was performed inconsistently, with CT brain being most common (74/85, 87.1%), while CT pulmonary angiogram (6/85, 7.1%), abdomen and pelvis (4/85, 4.7%) and aortogram (2/85, 2.4%) were done infrequently. Only four patients (4.7%) had all five neuroprotective goals of normoxia, normocarbia, normotension, normothermia and normoglycaemia achieved in the ED. The proportion of all five neuroprotective goals being met was significantly higher ( P = 0.01) among those with neurologically intact survival (3/13, 23.1%) than those without (1/72, 1.4%). CONCLUSION Post-resuscitation care at the ED showed great variability, indicating gaps between recommended guidelines and clinical practice. Good quality post-resuscitation care, centred around neuroprotection goals, must be initiated promptly to achieve meaningful survival with intact neurology.
Humans ; Out-of-Hospital Cardiac Arrest/mortality* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Emergency Service, Hospital ; Cardiopulmonary Resuscitation/methods* ; Return of Spontaneous Circulation ; Aged ; Adult ; Treatment Outcome ; Electrocardiography ; Tomography, X-Ray Computed ; Aged, 80 and over

INTRODUCTION: This study was conducted to evaluate the efficacy of postoperative computed tomography (CT) measurements of aortic lumen volumes in predicting aortic-related complications following acute type A aortic dissection (ATAAD) repair. METHODS: We conducted a single-institution retrospective aortic volumetric analysis of patients after ascending aorta replacement performed during 2001-2015. The volumetric measurements of total lumen (total-L), true lumen (TL), false lumen (FL), as well as the TL:FL ratio from the first and second postoperative computer angiograms were obtained. A generalised structural equation model was created to analyse the predictive utility of TL:FL ratio. RESULTS: One hundred and twenty-five patients underwent surgical intervention, of whom 97 patients were eventually discharged and analysed for postoperative complications. A total of 19 patients were included in the final analysis. Patients with late postoperative aortic complications had a significantly higher FL volume and total-L volume on the first (FL volume P = 0.041, total-L volume P = 0.05) and second (FL volume P = 0.01, total-L volume P = 0.007) postoperative scans. The odds of having aortic complications were raised by 1% with a 1 cm 3 increase in total-L volume and by 2% with a 1 cm 3 increase in FL volume. The TL:FL ratio was significantly lower in patients who developed complications. CONCLUSION Postoperative CT volumetric measurements in patients who developed complications are characterised by a significant increase in the FL volume and total-L volume from the first postoperative scans. Patients with disproportionately expanded FL presenting with TL:FL ratios less than 1 were associated with aortic complications. Hence, the TL:FL ratio may be a reliable and useful parameter to monitor postoperative disease progression and to evaluate the risk of late complications in ATAAD patients.
Humans ; Male ; Female ; Retrospective Studies ; Singapore ; Aortic Dissection/diagnostic imaging* ; Middle Aged ; Postoperative Complications/diagnostic imaging* ; Aged ; Tomography, X-Ray Computed ; Aortic Aneurysm/diagnostic imaging* ; Aorta/surgery* ; Adult ; Treatment Outcome ; Computed Tomography Angiography

OBJECTIVE: To explore the relationship between the effect of acupuncture and layered localization of computed tomography (CT) in treatment of lumbar disc herniation. METHODS: Based on the CT layered localization, the herniated lumbar discs were positioned in 5 layers, A, B, C, D and E among 300 patients with lumbar disc herniation. Combined with the horizontal and the frontal planes, the three-dimensional location was formed. Acupuncture was delivered at acupoints including bilateral Shenshu (BL23), Dachangshu (BL25), and Huantiao (GB30), Weizhong (BL40) on the affected side. One intervention of acupuncture was 30 min, once daily; 1 course of treatment was composed of 10 interventions and 2 courses were required. Before and after treatment, Japanese orthopaedic association (JOA) score was recorded, and the effect was evaluated. The curative effect was classified and compared with the CT layered localization. RESULTS: Of 300 patients, 226 cases were effective and the effective rate was 75.33%. The JOA scores of all patients, and in the effective group and the non-effective group were higher compared with the scores before treatment (P<0.05). With the layered localization considered, acupuncture was more effective on the cases positioned in C layer. Regarding the horizontal plane, the effect was better on the cases with zone 1 and zone 1-2 involved. In terms of the grade of frontal plane, acupuncture was more effective on the cases graded Ⅰ and Ⅱ. CONCLUSION The clinical effect of acupuncture on lumbar disc herniation is related with the layer and the horizontal zone of herniated disc positioned, as well as to the grade of the frontal plane.
Humans ; Acupuncture Therapy ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Middle Aged ; Adult ; Tomography, X-Ray Computed ; Lumbar Vertebrae/diagnostic imaging* ; Acupuncture Points ; Aged ; Young Adult ; Treatment Outcome

BACKGROUND: At present, biopsy is essential for the diagnosis of prostate cancer (PCa) before radical prostatectomy (RP). However, with the development of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and multiparametric magnetic resonance imaging (mpMRI), it might be feasible to avoid biopsy before RP. Herein, we aimed to explore the feasibility of avoiding biopsy before RP in patients highly suspected of having PCa after assessment of PSMA PET/CT and mpMRI. METHODS: Between December 2017 and April 2022, 56 patients with maximum standardized uptake value (SUVmax) of ≥4 and Prostate Imaging Reporting and Data System (PI-RADS) ≥4 lesions who received RP without preoperative biopsy were enrolled from two tertiary hospitals. The consistency between clinical and pathological diagnoses was evaluated. Preoperative characteristics were compared among patients with different pathological types, T stages, International Society of Urological Pathology (ISUP) grades, and European Association of Urology (EAU) risk groups. RESULTS: Fifty-five (98%) patients were confirmed with PCa by pathology, including 49 (89%) with clinically significant prostate cancer (csPCa, defined as ISUP grade ≥2 malignancy). One patient was diagnosed with high-grade prostatic intraepithelial neoplasia (HGPIN). CsPCa patients, compared with clinically insignificant prostate cancer (cisPCa) and HGPIN patients, were associated with a higher level of prostate-specific antigen (22.9 ng/mL vs . 10.0 ng/mL, P = 0.032), a lower median prostate volume (32.2 mL vs . 65.0 mL, P = 0.001), and a higher median SUVmax (13.3 vs . 5.6, P <0.001). CONCLUSIONS It might be feasible to avoid biopsy before RP for patients with a high probability of PCa based on PSMA PET/CT and mpMRI. However, the diagnostic efficacy of csPCa with PI-RADS ≥4 and SUVmax of ≥4 is inadequate for performing a procedure such as RP. Further prospective multicenter studies with larger sample sizes are necessary to confirm our perspectives and establish predictive models with PSMA PET/CT and mpMRI.
Humans ; Male ; Prostatectomy/methods* ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Aged ; Positron Emission Tomography Computed Tomography/methods* ; Biopsy ; Multiparametric Magnetic Resonance Imaging ; Prostate-Specific Antigen/metabolism*

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.
Humans ; Large Language Models ; Tomography, X-Ray Computed

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.
Humans ; Artificial Intelligence ; Deep Learning ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Positron-Emission Tomography

Synthetic CT (sCT) generated from CBCT has proven effective in artifact reduction and CT number correction, facilitating precise radiation dose calculation. However, the quality of different regions in sCT images is severely imbalanced, with soft tissue region exhibiting notably inferior quality compared to others. To address this imbalance, we proposed a Multi-Task Attention Network (MuTA-Net) based on VGG-16, specifically focusing the enhancement of image quality in soft tissue region of sCT. First, we introduced a multi-task learning strategy that divides the sCT generation task into three sub-tasks: global image generation, soft tissue region generation and bone region segmentation. This approach ensured the quality of overall sCT image while enhancing the network's focus on feature extraction and generation for soft tissues region. The result of bone region segmentation task guided the fusion of sub-tasks results. Then, we designed an attention module to further optimize feature extraction capabilities of the network. Finally, by employing a results fusion module, the results of three sub-tasks were integrated, generating a high-quality sCT image. Experimental results on head and neck CBCT demonstrated that the sCT images generated by the proposed MuTA-Net exhibited a 12.52% reduction in mean absolute error in soft tissue region, compared to the best performance among the three comparative methods, including ResNet, U-Net, and U-Net++. It can be seen that MuTA-Net is suitable for high-quality sCT image generation and has potential application value in the field of CBCT guided adaptive radiation therapy.
Cone-Beam Computed Tomography/methods* ; Humans ; Image Processing, Computer-Assisted/methods* ; Artifacts ; Algorithms ; Bone and Bones/diagnostic imaging* ; Neural Networks, Computer