Anatomical alterations: biparietal thinning in antiquity. Review of published cases and a new case

BACKGROUND Generative AI is a rapidly advancing field within Artificial Intelligence with wide-ranging applications. In the medical science domain, machine learning and deep learning methods have already found extensive use. This study aims to conduct a comparative analysis of seven freely available pretrained text-to-image models to generate radiological images based on a single text prompt. The primary objective is to determine which among these models produces the most accurate radiological images. The research investigates the effectiveness of generative AI in the medical domain, particularly for generating radiological images. Several text-to-image models are tested, building on previous research that explored DALL-E 2's capabilities in understanding radiological images. By comparing the performance of different models on a single text prompt, the study provides valuable insights into their potential use in medical image generation. Through this investigation, the study seeks to benefit medical professionals, researchers, and the wider AI community. Identifying the most accurate text-to-image model could enhance medical imaging applications, leading to improved diagnostics and treatment planning. OBJECTIVE The objective of this article is to conduct a comparative study of seven existing pretrained text-to-image models available freely on the internet, with the specific aim of generating radiological images based on a single text prompt. The primary goal is to determine which of these text-to-image models is capable of generating the most accurate radiological images for medical applications. By evaluating the performance of various text-to-image models, the research aims to provide insights into the effectiveness of generative AI techniques in the medical domain. Specifically, the study seeks to identify the model that demonstrates superior capabilities in accurately translating textual descriptions into radiological images. The article seeks to contribute to the field of Generative AI, particularly in the context of medical science and radiological imaging. By comparing different models' outcomes on a single prompt, the research aims to offer valuable information for medical professionals and researchers, highlighting the potential applications and limitations of these text-to-image models in medical image generation. Ultimately, the objective of the article is to facilitate advancements in medical imaging technologies, leading to improved diagnostics and medical decision-making processes. Through its comparative analysis, the article endeavors to aid the AI community in selecting the most suitable text-to-image model for generating accurate and reliable radiological images. METHODS Model Selection: Seven existing pretrained text-to-image generative models available freely on the internet were chosen for the study. These models were specifically designed for generating images from textual descriptions. Model Descriptions: A brief description of each text-to-image model used in the study, along with their respective results, was provided to contextualize the findings. Image Generation: The prompt used for testing all models was: "Photorealistic MRI scan of human lungs suffering from pneumonia." Comparative Analysis: The results obtained from each model were compared and analyzed to determine which model produced the most accurate radiological image in response to the given prompt. Keeping in mind the actual imaging of the medical condition, we consulted a physiology expert to compare with the images generated by the seven different models. RESULTS The following comparison results were obtained after the consultation and are presented as follows: Dall-E 2 created the most realistic image of the lungs of a person suffering from pneumonia. It also shows the thoracic cavity with the heart in it which gives more accuracy to the image that is created. Dall-E 2 could also successfully show the difference between the left and right lung. It showed the septum which most of the models could not. Midjourney did a good job at showing the infection even though it failed to create the image as realistically as Dall-E 2. Midjourney did provide a clear image though. It might accurately show the spread of the infection as well. Min-dalle highlighted the infectious parts well, but it failed to give a more realistic image. Carefree Creator did well with the image of the thoracic cavity but it is not very reliable for the detection of infections. Big Sleep is a model which we are unsure of. If the white parts in between show the mucus congestion, then it did a nice job at showing the congestion of the lungs but did a poor job at showing the thoracic cavity. Aphantasia used bright colours which might help the detection of infections even though it failed to show the lungs and the infection accurately. Deep Daze produced a very complicated image which makes identifying parts of the body and the infection very difficult. CONCLUSIONS From the results above, we can conclude that the existing text-to-image generation models are not capable of generating radiological images with 100% accuracy. However, it must be mentioned that some of the models performed better than the others in specific cases. For eg, the image generated by DALL-E 2 was able to show the difference between the left and right lungs properly and also was able to show the thoracic cavity as compared to the image generated by Aphantasia which was able to showcase the detection of infections better than DALL-E 2 even though it failed to show the lungs accurately. This study also indicates the importance for the need of better visualisation of medical conditions in existing radiological methods. For example the use of colours to better showcase the detection of infections as shown by the image generated by Aphantasia. Of course there are many other factors which must be considered while designing a visualisation method and we aren’t suggesting any particular method which needs to be implemented immediately. Proper consultation with an expert is always the first step. These results surely are a starting step in the domain of image generation for radiological images.It is true that in this study we used only one prompt. Further steps would include giving a better text prompt of the medial condition and giving prompts of more varied medical conditions. There are a variety of applications and benefits of generating radiological images. Many Machine Learning tasks like classification and segmentation require a large dataset for training respective models appropriately and an accurate radiological image generated using AI would help in making the dataset of the required size. Further developments in this field can lead to generating radiological images of specific conditions based on the particular prompt of the user.

Endoscopic retrograde cholangiopancreatography (ERCP) in patients with surgically altered anatomy is challenging. Several operative interventions of both the gastrointestinal tract and the biliary and/or pancreatic system lead to altered anatomy, rendering ERCP more difficult or even impossible with a conventional side-viewing duodenoscope. Adapted endoscopes are available to reach the biliopancreatic system and to perform ERCP in patients with altered anatomy. However, both technical difficulties and complications determine the procedure's success. Different technical approaches have been described and are highly dependent on local expertise and endoscopic equipment. Standardized practical guidelines are currently unavailable. This review focuses on the challenges encountered during ERCP in patients with altered anatomy and how to deal with them. The first challenge is reaching the papilla or the bilioenteric/pancreatoenteric anastomosis in the patient with postoperative altered anatomy. The second challenge is the cannulation of the biliopancreatic system and performing all conventional ERCP interventions and the third challenge is the control of possible complications. The available literature data on this topic is reviewed and illustrated with clinical cases.

Verify the presence and the molecular identity of phytoplasmas in Northern and Central Italy vineyards where yellows diseases are widespread. Phytoplasma presence and identity were determined by PCR/RFLP analyses on 16S ribosomal gene testing 1424 symptomatic samples. The 65% of samples resulted phytoplasma infected; in particular 256 samples were found positive to phytoplasmas belonging to group 16SrV (mainly Flavescence dorée associated), and the remaining 37% was infected by phytoplasmas belonging to ribosomal subgroup 16SrXII-A (Stolbur or Bois Noir associated). 16SrV ribosomal group representative strains were further typed for variability in SecY and rpS3 genes. The results showed the presence of phytoplasmas belonging to 16SrV-C, 16SrV-D and to a lesser extent, 16SrV-A subgroup. Possible relationships between genetic polymorphisms of phytoplasma strains belonging to subgroup 16SrV-C and their geographic distribution and/or epidemic situations were detected. Bois Noir and Flavescence dorée phytoplasmas are present in significant percentages in the areas under investigation. Molecular tools allowed to identify phytoplasma-infected plants and the genes employed as polymorphism markers resulted useful in distinguishing and monitoring the spreading of the diseases associated with diverse phytoplasmas belonging to 16SrV subgroup in vineyards.

IGHV3-21–using chronic lymphocytic leukemia (CLL) is a distinct entity with restricted immunoglobulin gene features and poor prognosis and is more frequently encountered in Northern than Southern Europe. To further investigate this subset and its geographic distribution in the context of a country (Italy) with both continental and Mediterranean areas, 37 IGHV3-21 CLLs were collected out of 1076 cases enrolled by different institutions from Northern or Central Southern Italy. Of the 37 cases, 18 were identified as homologous (hom)HCDR3–IGHV3-21 CLLs and were found almost exclusively (16 of 18) in Northern Italy; in contrast, 19 nonhomHCDR3–IGHV3-21 cases were evenly distributed throughout Italy. Clinically, poor survivals were documented for IGHV3-21 CLLs as well as for subgroups of mutated and homHCDR3–IGHV3-21 CLLs. Negative prognosticators CD38, ZAP-70, CD49d, and CD79b were expressed at higher levels in homHCDR3 than nonhomHCDR3–IGHV3-21 cases. Differential gene expression profiling (GEP) of 13 IGHV3-21 versus 52 non–IGHV3-21 CLLs identified, among 122 best-correlated genes, TGFB2 and VIPR1 as down- and up-regulated in IGHV3-21 CLL cases, respectively. Moreover, GEP of 7 homHCDR3 versus 6 nonhomHCDR3–IGHV3-21 CLLs yielded 20 differentially expressed genes, with WNT-16 being that expressed at the highest levels in homHCDR3–IGHV3-21 CLLs. Altogether, IGHV3-21 CLLs, including those with homHCDR3, had a peculiar global phenotype in part explaining their worse clinical outcome.

Phyllodes tumor (PT) is a rare tumor that can present as benign, borderline, or malignant. These tumors arise from the breast stroma, similar to fibroadenomas. Phyllodes tumors and fibroadenomas often have overlapping features in both radiological imaging and pathologic analysis. As a result, these two lesions are often difficult to differentiate and require the correlation of multiple modalities, including clinical context, radiologic imaging, and histological evaluation. This article presents a case of a borderline phyllodes tumor in a 51-year-old female, with the inclusion of its radiologic and pathologic images and performed treatment. The goal of this article is to provide a review of the clinical presentation, diagnostic imaging and pathology features, treatment, and management of a phyllodes tumor and compare and contrast this against the more common fibroadenomas, in order to provide aid for differentiating these two breast lesions.

Artificial intelligence (AI) incorporation into healthcare has proven revolutionary, especially in radiotherapy, where accuracy is critical. The purpose of the study is to present patterns and develop topics in the application of AI to improve the precision of anatomical diagnosis, delineation of organs, and therapeutic effectiveness in radiation and radiological imaging. We performed a bibliometric analysis of scholarly articles in the fields starting in 2014. Through an examination of research output from key contributing nations and institutions, an analysis of notable research subjects, and an investigation of trends in scientific terminology pertaining to AI in radiology and radiotherapy. Furthermore, we examined software solutions based on AI in these domains, with a specific emphasis on extracting anatomical features and recognizing organs for the purpose of treatment planning. Our investigation found a significant surge in papers pertaining to AI in the fields since 2014. Institutions such as Emory University and Memorial Sloan-Kettering Cancer Center made substantial contributions to the development of the United States and China as leading research-producing nations. Key study areas encompassed adaptive radiation informed by anatomical alterations, MR-Linac for enhanced vision of soft tissues, and multi-organ segmentation for accurate planning of radiotherapy. An evident increase in the frequency of phrases such as 'radiomics,' 'radiotherapy segmentation,' and 'dosiomics' was noted. The evaluation of AI-based software revealed a wide range of uses in several subdisciplinary fields of radiation and radiology, particularly in improving the identification of anatomical features for treatment planning and identifying organs at risk. The incorporation of AI in anatomical diagnosis in radiological imaging and radiotherapy is progressing rapidly, with substantial capacity to transform the precision of diagnoses and the effectiveness of treatment planning.

Psychometric analysis of questions associated with radiological images in the competitive examination for access to residency programs in Spain

The article is dedicated to identifying the common patterns of political system development in the city-states of medieval Italy and the urban republics of the Russian Northwest - Novgorod and Pskov. Alongside the Novgorod Republic, which existed from the 12th to the 15th centuries, trading city-states flourished in Northern Italy during nearly the same chronological period (from the late 11th century to approximately the mid-15th century). These included not only the well-known Venice or Genoa but also hundreds of larger and smaller republics, the history of which demonstrates similar processes to those occurring in the Russian popular governments, hundreds of kilometers away from them. Based on specific historical material, the evolution of these republics is examined, and comparisons are made with the well-known republic of Great Novgorod. The method of comparative historical analysis, utilizing a broad historical material, allows for conclusions to be drawn about the common laws of evolution in the political entities being examined. For the first time in the historiography of this problem, the method of comparative analysis is applied to a wide range of material, meaning that Great Novgorod is compared not exclusively with Venice or any other single Italian republic, but simultaneously with many of them, which allows for a broader view of the picture and helps to identify regularities in political development and understand the current place of the Novgorod Republic in European and world history. By examining the political evolution of the Novgorod Republic and a significant number of city-states in northern medieval Italy, a conclusion is made about the identical nature of changes in the power structures of Italian and Russian republics, based on which the commonality of the evolutionary path of medieval republicanism is asserted. Numerous differences in the political systems of Italian city-states and Great Novgorod do not allow for judgments about the backwardness or any other "defects" of the Novgorod polity.

BackgroundA solitary diverticulum of the caecum is a rare benign condition which was first described by Potier in 1912 [1]. Clinical symptoms are usually a manifestation of complications arising from inflammation, perforation or haemorrhage. Despite radiological imaging, a pre-operative diagnosis is infrequent.Case presentationWe report two cases of right iliac fossa pain associated with a solitary caecal diverticulum. We discuss the clinical presentation, investigative modalities, and current therapeutic guidelines associated with this rare condition and highlight the difference from the more common conditions of appendicitis in the young and caecal neoplasms in the older patient.ConclusionComplications of a solitary caecal diverticulum should be considered in the differential diagnosis of acute right lower quadrant pain. Mild caecal diverticulitis verified pre-operatively by radiological imaging or laparoscopically can be ameliorated by antibiotics alone. However, severe inflammation, perforation, haemorrhage or torsion necessitates a localised or radical resection. The presence of multiple diverticula, caecal phlegmon, or the inability to rule out an underlying caecal neoplasm warrants a right hemicolectomy.

The present study examines the prehistoric human skeletal remains retrieved starting from the 1920s in the deposit of the Farneto rock shelter, situated in the area of the ‘Parco dei Gessi Bolognesi e Calanchi dell’Abbadessa’ (San Lazzaro di Savena, Bologna, northern Italy). An exact dating and a reliable interpretation of the assemblage had not been reached so far because of the lack of contextual data useful for dating purposes, the inaccurate recovery procedures of the remains and their state of preservation. In fact, the skeletal remains from the Farneto rock shelter are highly fragmented and commingled, whereas reliable information about their original position and their recovery procedures are not available. Despite these difficulties, radiocarbon analyses allowed the precise dating of the remains to a final phase of the Neolithic and an early phase of the Eneolithic period in Emilia Romagna (northern Italy). The study of the assemblage enabled to clarify the use of the context for funerary purposes. Moreover, the anthropological and taphonomic analyses of the skeletal remains shed light on the biological profile of the individuals and on some events that occurred after their death. In particular, the analysis of perimortem lesions highlighted the existence of intentional interventions related to corpse treatment, referable to dismembering/disarticulation and scarnification, i.e. cleaning of bones from soft tissues. Finally, the comparison with other Italian and European Neo/Eneolithic funerary contexts enabled a better understanding of these complex ritual practices.

The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the inherent complexities of 2D and 3D radiologic data present unique challenges that existing models, which are typically pretrained on general nonmedical images, do not adequately address. To bridge this gap and harness the diagnostic precision required in radiologic imaging, we introduce radiologic contrastive language-image pretraining (RadCLIP): a cross-modal vision-language foundational model that utilizes a vision-language pretraining (VLP) framework to improve radiologic image analysis. Building on the contrastive language-image pretraining (CLIP) approach, RadCLIP incorporates a slice pooling mechanism designed for volumetric image analysis and is pretrained using a large, diverse dataset of radiologic image-text pairs. This pretraining effectively aligns radiologic images with their corresponding text annotations, resulting in a robust vision backbone for radiologic imaging. Extensive experiments demonstrate RadCLIP's superior performance in both unimodal radiologic image classification and cross-modal image-text matching, underscoring its significant promise for enhancing diagnostic accuracy and efficiency in clinical settings. Our key contributions include curating a large dataset featuring diverse radiologic 2D/3D image-text pairs, pretraining RadCLIP as a vision-language foundation model on this dataset, developing a slice pooling adapter with an attention mechanism for integrating 2D images, and conducting comprehensive evaluations of RadCLIP on various radiologic downstream tasks.

Application of artificial intelligence in respiratory medicine: Has the time arrived?

Adaptation is an eternal theme of biological evolution. The paper aims at exploring the conception of positive correlation between traditional Chinese medicine (TCM) and human homeostatic evolution based on medical perspective. Discussions mainly involve TCM conforming to natural laws and natural evolution of life, spontaneous harmonization of yin and yang and operating system of human self-healing, modern human immunology and human endogenous immune function in TCM, self-homeostasis of human micro-ecological state and balance mechanism on regulating base in TCM, as well as adaptation-eternal theme of biological evolution and safeguarding adaptability-value of TCM. In perspective of medicine, theory and practice of TCM are in positive correlation with human homeostatic evolution, and what TCM tries to maintain is human intrinsic adaptive capability to disease and nature. Therefore, it is the core value of TCM, which is to be further studied, explored, realized and known to the world.

Análisis psicométrico de las preguntas asociadas a imágenes radiológicas en el examen para médico interno residente en España

Vertebral infection in a male individual buried in the monastic cemetery (Cemetery 2) at Ghazali (ca. 670–1270 CE), northern Sudan