BACKGROUND: The automatic generation of radiology reports, which seeks to create a free-text description from a clinical radiograph, is emerging as a pivotal intersection between clinical medicine and artificial intelligence. Leveraging natural language processing technologies can accelerate report creation, enhancing health care quality and standardization. However, most existing studies have not yet fully tapped into the combined potential of advanced language and vision models.
OBJECTIVE: The purpose of this study was to explore the integration of pretrained vision-language models into radiology report generation. This would enable the vision-language model to automatically convert clinical images into high-quality textual reports.
METHODS: In our research, we introduced a radiology report generation model named ClinicalBLIP, building upon the foundational InstructBLIP model and refining it using clinical image-to-text data sets. A multistage fine-tuning approach via low-rank adaptation was proposed to deepen the semantic comprehension of the visual encoder and the large language model for clinical imagery. Furthermore, prior knowledge was integrated through prompt learning to enhance the precision of the reports generated. Experiments were conducted on both the IU X-RAY and MIMIC-CXR data sets, with ClinicalBLIP compared to several leading methods.
RESULTS: Experimental results revealed that ClinicalBLIP obtained superior scores of 0.570/0.365 and 0.534/0.313 on the IU X-RAY/MIMIC-CXR test sets for the Metric for Evaluation of Translation with Explicit Ordering (METEOR) and the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluations, respectively. This performance notably surpasses that of existing state-of-the-art methods. Further evaluations confirmed the effectiveness of the multistage fine-tuning and the integration of prior information, leading to substantial improvements.
CONCLUSIONS: The proposed ClinicalBLIP model demonstrated robustness and effectiveness in enhancing clinical radiology report generation, suggesting significant promise for real-world clinical applications.

Rupture of the azygous vein may result from abrupt deceleration applied to the mobile azygous arch, which can initiate shearing forces within the thorax.

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Monkeypox is a zoonotic disease, endemic in central and west African regions, and has re-emerged, currently causing an outbreak as of May 2022. In this systematic review, we aimed to characterize the current face of the disease, with a detailed categorization of mucocutaneous, as well as systemic symptoms of the disease. We searched four main online databases with the keywords \"monkeypox\" and \"Orthopoxvirus\". A total of 46 articles were included, with a cumulative number of 1984 confirmed cases. Patients were predominantly men who have sex with men, who were mostly in their 30s, with a history of unprotected sexual contact or international travel. Among mucocutaneous manifestations, anogenital lesions were the most commonly observed, followed by lesions on the limbs, face, trunk, and palms or soles. Among lesion types, vesiculopustular, pustular or pseudo-pustular, vesicular-umbilicated and papular lesions were the most common, mainly presenting asynchronously, with less than 10 lesions on each patient. Almost all patients also reported systemic manifestations, namely fever, lymphadenopathy, fatigue, myalgia, headaches, pharyngitis, and proctitis. Sexual contact is the main pathway of transmission in the current outbreak, with viral shedding in bodily fluids playing a key role. We\'ve compared these idiosyncratic findings of the new outbreak with previous outbreaks. We\'ve also gathered and categorized images from our included studies to make a \"clinical atlas\" for this \"new\" face of monkeypox, which can be of utmost importance for clinicians to be familiarized with, and have a clear picture of monkeypox for their differential diagnoses.

UNASSIGNED: Detection of circulating tumor cells (CTCs) is a promising technology in tumor management; however, the slow development of CTC identification methods hinders their clinical utility. Moreover, CTC detection is currently challenging owing to major issues such as isolation and correct identification. To improve the identification efficiency of glioma CTCs, we developed a karyoplasmic ratio (KR)-based identification method and constructed an automatic recognition algorithm. We also intended to determine the correlation between high-KR CTC and patients\' clinical characteristics.
UNASSIGNED: CTCs were isolated from the peripheral blood samples of 68 glioma patients and analyzed using DNA-seq and immunofluorescence staining. Subsequently, the clinical information of both glioma patients and matched individuals was collected for analyses. ROC curve was performed to evaluate the efficiency of the KR-based identification method. Finally, CTC images were captured and used for developing a CTC recognition algorithm.
UNASSIGNED: KR was a better parameter than cell size for identifying glioma CTCs. We demonstrated that low CTC counts were independently associated with isocitrate dehydrogenase (IDH) mutations (p = 0.024) and 1p19q co-deletion status (p = 0.05), highlighting its utility in predicting oligodendroglioma (area under the curve = 0.770). The accuracy, sensitivity, and specificity of our algorithm were 93.4%, 81.0%, and 97.4%, respectively, whereas the precision and F1 score were 90.9% and 85.7%, respectively.
UNASSIGNED: Our findings remarkably increased the efficiency of detecting glioma CTCs and revealed a correlation between CTC counts and patients\' clinical characteristics. This will allow researchers to further investigate the clinical utility of CTCs. Moreover, our automatic recognition algorithm can maintain high precision in the CTC identification process, shorten the time and cost, and significantly reduce the burden on clinicians.

Convolutional neural networks (CNNs) have demonstrated expert-level performance in cutaneous tumour classification using clinical images, but most previous studies have focused on dermatologist-versus-CNN comparisons rather than their combination. The objective of our study was to evaluate the potential impact of CNN assistance on dermatologists for clinical image interpretation.
A multi-class CNN was trained and validated using a dataset of 25,773 clinical images comprising 10 categories of cutaneous tumours. The CNN\'s performance was tested on an independent dataset of 2107 images. A total of 400 images (40 per category) were randomly selected from the test dataset. A fully crossed, self-control, multi-reader multi-case (MRMC) study was conducted to compare the performance of 18 board-certified dermatologists (experience: 13/18 ≤ 10 years; 5/18＞10 years) in interpreting the 400 clinical images with or without CNN assistance.
The CNN achieved an overall accuracy of 78.45% and kappa of 0.73 in the classification of 10 types of cutaneous tumours on 2107 images. CNN-assisted dermatologists achieved a higher accuracy (76.60% vs. 62.78%, P < 0.001) and kappa (0.74 vs. 0.59, P < 0.001) than unassisted dermatologists in interpreting the 400 clinical images. Dermatologists with less experience benefited more from CNN assistance. At the binary classification level (malignant or benign), the sensitivity (89.56% vs. 83.21%, P < 0.001) and specificity (87.90% vs. 80.92%, P < 0.001) of dermatologists with CNN assistance were also significantly improved than those without.
CNN assistance improved dermatologist accuracy in interpreting cutaneous tumours and could further boost the acceptance of this new technique.

A man in his 40\'s was admitted to the general surgery ward with multiple episodes of large amounts of painless bright red per rectal (PR) bleeding and passage of clots. Urgent outpatient colonoscopy revealed a diverticulum which was associated with a wide diameter blood vessel originating from its base which was not actively bleeding. The clinical picture presented by the colonoscopy is one of the first to clearly identify large caliber blood vessels emerging from a colonic diverticulum.

Dicephalus parapagus are conjoined twins with two separate heads. Proper identification helps in patient counseling and timely referral to specialist centers.

A 42-year-old male presented with 3-month history of constant right-sided frontal headaches, severe right-sided intermittent sharp jaw pain, odynophagia, globus pharyngis, and worsening episodes of blurry vision in his right eye. Cervicocerebral angiography demonstrated a prominent, 4 cm right sided styloid process with close proximity to the right internal carotid artery (ICA). The patient was referred to otorhinolaryngology for styloidectomy and continued care.