Interpretation Of Radiological Images Research Articles

Chest radiology report generation based on cross-modal multi-scale feature fusion

Chest radiology imaging plays a crucial role in the early screening, diagnosis, and treatment of chest diseases. The accurate interpretation of radiological images and the automatic generation of radiology reports not only save the doctor's time but also mitigate the risk of errors in diagnosis. The core objective of automatic radiology report generation is to achieve precise mapping of visual features and lesion descriptions at multi-scale and fine-grained levels. Existing methods typically combine global visual features and textual features to generate radiology reports. However, these approaches may ignore the key lesion areas and lack sensitivity to crucial lesion location information. Furthermore, achieving multi-scale characterization and fine-grained alignment of medical visual features and report text features proves challenging, leading to a reduction in the quality of radiology report generation. Addressing these issues, we propose a method for chest radiology report generation based on cross-modal multi-scale feature fusion. First, an auxiliary labeling module is designed to guide the model to focus on the lesion region of the radiological image. Second, a channel attention network is employed to enhance the characterization of location information and disease features. Finally, a cross-modal features fusion module is constructed by combining memory matrices, facilitating fine-grained alignment between multi-scale visual features and reporting text features on corresponding scales. The proposed method is experimentally evaluated on two publicly available radiological image datasets. The results demonstrate superior performance based on BLEU and ROUGE metrics compared to existing methods. Particularly, there are improvements of 4.8% in the ROUGE metric and 9.4% in the METEOR metric on the IU X-Ray dataset. Moreover, there is a 7.4% enhancement in BLEU-1 and a 7.6% improvement in the BLEU-2 on the MIMIC-CXR dataset.

Compliance of endodontic treatment with clinical guidelines for dental care in periapical diseases

Aim. To assess compliance with clinical guidelines for periapical tissue diseases by dentists. Materials and methods. Evaluation of medical records in non-state medical organizations for compliance with the completeness of simple medical services was performed. In the process of evaluating the completeness of clinical guidelines, the sections of the requirements for outpatient diagnosis and treatment of periapical tissue diseases in complicated clinical cases were analyzed. A total of 9 cases of patients with periapical tissue pathology were analyzed. Results. Out of 22 simple therapeutic and diagnostic medical services (SMS) that should be performed for each patient with periapical tissue diseases, most of the mandatory PMU related to professional oral hygiene were not performed, or were performed in an insignificant number of cases. Recommendations for the selection of items and hygiene products integral to oral care education were given only to those patients (22.2%) who had a root apex resection surgery as part of the package of measures. In 100.0% of cases, dentists perform and record the following PMU: collection of anamnesis and complaints for oral pathology, visual examination for oral pathology, diagnosis of the state of the dentoalveolar system using methods and means of radiological imaging, description and interpretation of radiological images, instrumental and medicamentous treatment of root canal, root canal filling. Considering the requirements of clinical guidelines for outpatient diagnostics in periapical tissue diseases it must be noted that the following obligatory PMU are performed insufficiently: palpation of oral organs (33,3%), targeted intraoral contact radiography (33,3%), thermodiagnostics of tooth (11,1%). Conclusions. The conducted research has shown that there are considerable deviations from requirements of acting clinical recommendations in diagnostics and treatment of periapical tissue diseases which may be connected both with continuation of the steady treatment and diagnostic approaches existing for many decades and with lack of careful attitude of dentists to the new normative legal acts regulating their labor functions.

404. A SYSTEMATIC REVIEW AND META-ANALYSIS ON THE ROLE OF RADIOMICS IN ESOPHAGEAL CANCER: DIAGNOSIS, TREATMENT RESPONSE AND SURVIVAL PREDICTION

Abstract Background The use of artificial intelligence, in the form of radiomics, enables more detailed interpretation of radiological images in less time, when compared to the human eye. Some challenges in managing esophageal cancer, particularly those reliant of human factors, can be addressed by incorporating radiomics into image interpretation, thereby supporting diagnosis, treatment planning and predicting treatment response and survival. This systematic review and meta-analysis aims to summarize the evidence in this expanding field. Methods The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases—articles describing the use of radiomics in 18F-FDG PET/CT scans and CT scans in the esophageal cancer management were included. A meta-analysis was also performed. Results 50 studies were included, most of which were single center cohort studies. For the assessment of treatment response using 18F-FDG PET/CT scans, 7 studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1–90.6) and 87.1% (78.0–92.8). For the assessment of treatment response using CT scans, 5 studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4–90.7) and 76.1% (69.9–81.4). The remaining 38 studies discussed the benefits of radiomics in diagnosis, radiotherapy planning and survival prediction, often in less time and best when used in conjunction with radiologists. Conclusion This review explores the many possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Integrated clinical and radiomic models were the best at facilitating diagnosis and survival prediction, particularly when combined with radiologists. More multi-center studies are required to compare and externally validate models to build an evidence base.

Visual hindsight bias for abnormal mammograms in radiologists.

Hindsight bias-where people falsely believe they can accurately predict something once they know about it-is a pervasive decision-making phenomenon, including in the interpretation of radiological images. Evidence suggests it is not only a decision-making phenomenon but also a visual perception one, where prior information about an image enhances our visual perception of the contents of that image. The current experiment investigates to what extent expert radiologists perceive mammograms with visual abnormalities differently when they know what the abnormality is (a visual hindsight bias), above and beyond being biased at a decision level. experienced mammography readers were presented with a series of unilateral abnormal mammograms. After each case, they were asked to rate their confidence on a 6-point scale that ranged from confident mass to confident calcification. We used the random image structure evolution method, where the images repeated in an unpredictable order and with varied noise, to ensure any biases were visual, not cognitive. Radiologists who first saw an original image with no noise were more accurate in the max noise level condition [area under the curve ] than those who first saw the degraded images (; difference: ), suggesting that radiologists' visual perception of medical images is enhanced by prior visual experience with the abnormality. Overall, these results provide evidence that expert radiologists experience not only decision level but also visual hindsight bias, and have potential implications for negligence lawsuits.

Laparoscopic Microwave Ablation: Which Technologies Improve the Results.

Liver resection is the best treatment for hepatocellular carcinoma (HCC) when resectable. Unfortunately, many patients with HCC cannot undergo liver resection. Percutaneous thermoablation represents a valid alternative for inoperable neoplasms and for small HCCs, but it is not always possible to accomplish it. In cases where the percutaneous approach is not feasible (not a visible lesion or in hazardous locations), laparoscopic thermoablation may be indicated. HCC diagnosis is commonly obtained from imaging modalities, such as CT and MRI, However, the interpretation of radiological images, which have a two-dimensional appearance, during the surgical procedure and in particular during laparoscopy, can be very difficult in many cases for the surgeon who has to treat the tumor in a three-dimensional environment. In recent years, more technologies have helped surgeons to improve the results after ablative treatments. The three-dimensional reconstruction of the radiological images has allowed the surgeon to assess the exact position of the tumor both before the surgery (virtual reality) and during the surgery with immersive techniques (augmented reality). Furthermore, indocyanine green (ICG) fluorescence imaging seems to be a valid tool to enhance the precision of laparoscopic thermoablation. Finally, the association with laparoscopic ultrasound with contrast media could improve the localization and characteristics of tumor lesions. This article describes the use of hepatic three-dimensional modeling, ICG fluorescence imaging and laparoscopic ultrasound examination, convenient for improving the preoperative surgical preparation for personalized laparoscopic approach.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians' workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

Ovarian dysgerminoma: clues to the radiological diagnosis.

Ovarian dysgerminoma (OD) is a rare germ cell tumor accounting for 1%-2% of all malignant ovarian tumors and is generally associated with a good prognosis. The condition is more frequent in young women and can arise in dysgenetic gonads that contain gonadoblastomas. While the definitive diagnosis of OD is only possible histologically, certain radiological features can provide facilitating clues. A large, unilateral, solid, lobulated ovarian tumor with markedly enhancing septa should raise the suspicion of OD in young women. Serum lactate dehydrogenase is characteristically elevated in this tumor type and can complement its diagnosis and postoperative follow-up; however, it is a nonspecific marker. Moreover, knowing the mimickers of OD is essential to optimizing the radiological image interpretation and allowing for adequate management and timely treatment. Therefore, in this article, the radiological and clinical-pathologic features of ODs were reviewed to allow radiologists to become familiarized with them and narrow the diagnostic possibilities when facing this type of tumor.

Should Radiological Assessment be Part of Core General Surgery Competency? A Survey of the General Surgery Residents

Introduction: Radiological assessment is an important skill to develop in general surgery training. Therefore, we aimed to determine general surgery residents’ points of view on receiving formal radiology didactics. Methods: We performed an anonymous survey of general surgery residents throughout the US. The survey queried the residents` postgraduate year, training program type, diagnostic radiology education in their training program, as well as the residents’ comfort level in interpreting various imaging modalities, followed by a series of images to assess the residents’ ability to interpret images showing various surgical disease processes. Results: A total of 365 residents responded to the survey. 76.6% of the respondent states that there is no structured didactic session in their program on radiological studies. However, 66.3% felt that interpretation of radiological images should be used to determine surgical competency and promotion to the next academic year. In terms of accurately reading images - 68.7% of the residents were able to read an x-ray showing cecal volvulus correctly, 51.9% were able to read a cholangiogram correctly, and 95.3% were correctly read an x-ray showing free under the diaphragm. Conclusion: Most residents favored having radiological assessments as part of the competency evaluation. Furthermore, a curriculum and inbuilt training structure that aims to ensure residents develop competent clinical image interpretation abilities may enhance the development and retention of such skills, ultimately influencing patient outcomes.

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

The efficient and accurate interpretation of radiologic images is paramount. To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities. This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH). The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding. A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001). These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.

A cadaveric study on variation in branching pattern of common carotid artery

Carotid arterial system are vessels of head and neck region. Comprehensive understanding on its branching configuration is of considerable importance to avoid accidental injury during surgical interventions. Common carotid artery (CCA) usually bifurcates into external carotid artery (ECA) and internal carotid artery (ICA) at level of upper border of thyroid cartilage. Normally ECA lies anteromedial to ICA at its commencement and provides branches, but ICA does not provide any branches in neck. In this case, right CCA bifurcated above cricoid cartilage and divided into two branches of equal diameter in a middle-aged female cadaver. Medial one does not provide any branch and it appeared pale. It continued upward, entered carotid canal and it was confirmed as ICA. The ECA was found lateral to right ICA both in its origin and course. ECA provided lingual, facial and occipital branches. Superior thyroid artery was not identified, and it might be accidently damaged during dissection. It was noticed ECA was a content of carotid sheath. Vagus nerve first found between IJV and CCA and subsequently located between IJV and ECA. Third aortic arch contributed to development of CCA, leading part of ICA and entire part of ECA. Remaining part of ICA was developed from cranial portion of dorsal aorta. Therefore, any embryological deficiency in third aortic arch, dorsal aorta or changes inside of origin of ECA may lead to alteration in position of these vessels. Understanding vascular anatomy of neck is vital in precise interpretation of radiological images and neck surgeries.

The Application of Eye-Tracking Technology in the Assessment of Radiology Practices: A Systematic Review

The aim of this review is to provide an in-depth analysis of literature pertaining to the use of eye-tracking equipment in the evaluation of radiological image interpretation by professionals in clinical practice. A systematic search of current literature was conducted through the databases of CINAHL, Medline, ProQuest, PubMed, Scopus, Web of Science and Wiley Online Library. A total of 25 articles were included in the final analysis. The literature gathered referenced four main discussions, which were competency assessment, educational tools, visual search behaviour and assistive aid evaluations. The majority of articles (68%) referenced to the competency assessment of professional groups yet appeared to have conflicting results within the categories of speed and eye-metrics. Significant conclusions could be made pertaining to confidence (100%) and accuracy measurements (56%), which suggested a background of higher experience correlates to a higher rate of accuracy and a higher confidence level. Other findings regarding the main themes focused on eye-tracking as an educational tool, where the literature suggests that such equipment may be useful in improving educational repertoire and interpretation technique. Literature pertaining to the visual search behaviour analysis and the evaluation of assistive aids did not provide strong conclusions due to research limitations. Whilst the use of eye-tracking in the analysis of radiological practices is a promising new venture to quantify the interpretation patterns of professionals, undertaking future research is recommended to solidify conclusions and provide greater insight.

Influence of smoking on the gastrointestinal tract : Range of findings

HintergrundNikotin ist ein hochwirksames Suchtgift, das bei regelmäßiger Einnahme chronische oder unheilbare Erkrankungen und somit eine eingeschränkte Lebensqualität zur Folge haben kann.FragestellungDas Ziel dieser Übersichtsarbeit besteht darin, mögliche gesundheitliche Folgen des Rauchens auf den Gastrointestinaltrakt aufzuzeigen und einen Überblick über raucherassoziierte neoplastische und nichtneoplastische gastrointestinale Erkrankungen zu geben.Material und MethodeAnhand einer ausführlichen Literaturrecherche wird der aktuelle Wissensstand zu raucherassoziierten Folgen auf den Gastrointestinaltrakt dargestellt.ErgebnisseRauchen ist ein wesentlicher Risikofaktor für die Entstehung neoplastischer und nichtneoplastischer Erkrankungen des gesamten Gastrointestinaltrakts. Diese weisen in der radiologischen Bildgebung allerdings keine spezifischen, raucherassoziierten Merkmale auf.SchlussfolgerungDie Kenntnis einer Raucheranamnese sowie möglicher Auswirkungen von Nikotin auf den Gastrointestinaltrakt können in der radiologischen Bildinterpretation hilfreich sein sowie die diagnostische Entscheidungsfähigkeit und Genauigkeit verbessern.

Should Radiological Assessment be Part of Core General Surgery Competency? A Survey of the General Surgery Residents.

Radiological assessment is an important skill to develop in general surgery training. Therefore, we aimed to determine general surgery residents' points of view on receiving formal radiology didactics. We performed an anonymous survey of general surgery residents throughout the USA. The survey queried the residents' postgraduate year, training program type, diagnostic radiology education in their training program, as well as the residents' comfort level in interpreting various imaging modalities, followed by a series of images to assess the residents' ability to interpret images showing various surgical disease processes. A total of 365 residents responded to the survey. In total, 76.6% of the respondent states that there is no structured didactic session in their program on radiological studies. However, 66.3% felt that interpretation of radiological images should be used to determine surgical competency and promotion to the next academic year. In terms of accurately reading images-68.7% of the residents were able to read an X-ray showing cecal volvulus correctly, 51.9% were able to read a cholangiogram correctly, and 95.3% were correctly read an X-ray showing free under the diaphragm. Most residents favored having radiological assessments as part of the competency evaluation. Furthermore, a curriculum and inbuilt training structurethat aimsto ensure residents develop competentclinicalimage interpretationabilitiesmay enhance the development and retention ofsuchskills, ultimately influencing patient outcomes.

Quantitative analysis of pulmonary structures in PMCT; Stereological comparison of drowning compared to opioid-overdose cases.

• Stereology can be applied on post mortem CT scans of the lungs. • The method provides information of volume and attenuation of lung structures • No difference in volume and attenuation in lungs of drowning victims and controls The aim of this study, was to introduce stereology as a versatile and robust tool for quantitative image analysis of volume and attenuation characteristics (Hounsfield Units (HU's)), in a blinded control case study investigating lungs of drowning victims compared to a control group on post mortem computed tomography (PMCT) data. PMCT scans of the lungs from 14 drowned cases and 14 matched opioid-overdose-controls was included. Quantitative CT-analysis was performed using a stereological approach adapted to PMCT data that allowed for precise extraction of volume and HU-values using stereological point-probes assigned manually to individual lung-structures. Qualitative radiological image interpretation performed by a trained radiologist was compared to the quantitative analyses. No significant difference was found for total lung volume, volumes of consolidations, ground glass opacities, bronchi, and air-filled lung tissue. When comparing drowning cases with opioid overdose cases as controls, the extracted HU-values did not show statistically significant changes in mean attenuation characteristics. No major discrepancies were found between the quantitative analysis and qualitative analysis. Conventional radiological evaluation of PMCT images rely on the radiologists’ ability to distinguish normal from pathological. Quantitative image analysis offers, to name a few, precise estimations of structure volume and HU-statistics. Although the used 14 matched cases data failed to significantly aid the diagnosis of drowning statistically, we envisage that quantitative PMCT analysis using stereology could become a valuable tool to improve objective forensic radiological interpretation.

An overview of maxillofacial radiology education activities during and after the COVID- 19 pandemic

Objective: To convey the applications of online education in the field of maxillofacial radiology, which is widely used in the world during the COVID- 19 pandemic, and the post-pandemic education and training models suggestions in accordance with the changing world order by compiling the research results and literature information on the subject. Method: Different educational models applied in maxillofacial radiology before and during the COVID-19 pandemic were evaluated in terms of their results and activities by looking into published articles. In the light of these data, regulations, and recommendations were developed for post-pandemic educational activities. Findings: During the COVID-19 pandemic, the disruptions in educational activities at all levels and fields all over the world were reflected in maxillofacial radiology education. However, studies show that learning with online education in this field, especially in the interpretation of radiological images and problem-based learning activities, is at least as successful as or more than traditional education models. Online education also provides feedback to lecturers and promotes active learning, considered best practices for the teaching-learning process. During the COVID-19 pandemic period, theoretical education has been tried to be continued in the form of online education, although various some studies showed that online education is insufficient in practical education. In radiological interpretation, it is one of the biggest problems to ensure students' attention and effective participation in practical terms. In addition, online teaching of radiographic techniques and applications may not be as effective as instructive teaching techniques. After the pandemic, it is predicted that returning to normal maxillofacial radiology education will be challenging in both theoretical and practical aspects and there will is be a need to change. Results: Considering the strengths and weaknesses of online education models, whose application area has increased so much during the COVID-19 pandemic. The development and implementation of new, effective education-training models suitable both for the conditions and for everyone involved in education for the post-pandemic period, emerges as a necessity. After the pandemic, it is important to evaluate the blended education model in theoretical education, to make various changes in maxillofacial radiology practical training and to re-determine the rules of hygiene, separation and education.

Visual Hindsight Bias for Mammogram Abnormalities in Expert Radiologists

Hindsight bias – where people believe they can accurately predict something only once they know about it – is a pervasive decision making phenomenon, including in interpretation of radiological images (e.g., Berlin, 2000). However, some evidence suggests it is not only a decision making phenomena but also a visual perception one, where prior information about an image genuinely enhances our visual perception of that image (Harley et al. 2004). For example, the random image structure evolution (RISE) method of Sadr & Sinha (2004), where objects appear in random order embedded in different amounts of noise, demonstrates that primed images are genuinely recognized more easily than those that have not been seen before, a visual hindsight bias. The current experiment tests to what extent expert radiologists show a visual hindsight bias to mammograms with visible abnormalities when they are embedded in noise (e.g., to what extent they see the images differently when they know what the abnormality is, rather than just being biased at a decision level). N=40 experienced mammograph readers were presented with a series of unilateral abnormal mammograms (half had masses, half had calcifications), for three seconds each. After the presentation of each image, observers were asked to rate their confidence on a 6-point scale ranging from confident mass to confident calcification. Critically, we used the RISE method where the images in a sequence were repeated in an unpredictable order, and with varied noise. We found that radiologists who first saw a clear, original image were more accurate in the max noise level condition (d’=0.49) than those who first saw the degraded images (d’=0.20; p=0.017), suggesting that radiologists’ visual perception of medical images is enhanced by prior knowledge of the abnormality. Overall, these results provide preliminary evidence that radiologists experience not only decision level but also visual hindsight bias.

Congenital Urogenital Tract Anomalies are Associated with a Higher Rate of Radiologic Interpretation Error

Objective: Congenital Urogenital Tract Anomalies (CUTA) are complex and proper diagnosis is important for competent patient care. We set out to investigate whether there is an increased rate of radiologic image interpretation error in patients with CUTA. Methods: We utilized a case-control study design to compare each CUTA case (n=30) with 6 age-matched controls (n=180). We evaluated electronic medical records obtained from EPIC of patients who were referred to our tertiary care center for a higher level of care. We compared imaging results from MRI, CT, and US to intraoperative findings to look for any discrepancies in four key anatomical areas (vagina, uterus, ovaries, and urologic-kidney/ureter/bladder). Error was determined using a standardized scoring system for adjudicating imaging discrepancies in each anatomic area. Results: Cases and controls were similar across all demographic variables except for weight, (controls had a higher BMI). The rate of radiologic interpretation error was higher in patients with congenital urogenital tract anomalies when compared to age-matched controls (OR=51.65; 95% CI (6.39, 417.42)). Conclusion: CUTA are associated with a high rate of radiologic interpretation error. Although radiologic scans are helpful in the work up of CUTA, imaging results may not be definitively diagnostic.

Holistic processing only? The role of the right fusiform face area in radiological expertise.

Radiologists can visually detect abnormalities on radiographs within 2s, a process that resembles holistic visual processing of faces. Interestingly, there is empirical evidence using functional magnetic resonance imaging (fMRI) for the involvement of the right fusiform face area (FFA) in visual-expertise tasks such as radiological image interpretation. The speed by which stimuli (e.g., faces, abnormalities) are recognized is an important characteristic of holistic processing. However, evidence for the involvement of the right FFA in holistic processing in radiology comes mostly from short or artificial tasks in which the quick, 'holistic' mode of diagnostic processing is not contrasted with the slower 'search-to-find' mode. In our fMRI study, we hypothesized that the right FFA responds selectively to the 'holistic' mode of diagnostic processing and less so to the 'search-to-find' mode. Eleven laypeople and 17 radiologists in training diagnosed 66 radiographs in 2s each (holistic mode) and subsequently checked their diagnosis in an extended (10-s) period (search-to-find mode). During data analysis, we first identified individual regions of interest (ROIs) for the right FFA using a localizer task. Then we employed ROI-based ANOVAs and obtained tentative support for the hypothesis that the right FFA shows more activation for radiologists in training versus laypeople, in particular in the holistic mode (i.e., during 2s trials), and less so in the search-to-find mode (i.e., during 10-s trials). No significant correlation was found between diagnostic performance (diagnostic accuracy) and brain-activation level within the right FFA for both, short-presentation and long-presentation diagnostic trials. Our results provide tentative evidence from a diagnostic-reasoning task that the FFA supports the holistic processing of visual stimuli in participants' expertise domain.

Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases.

Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.

A morphology-based radiological image segmentation approach for efficient screening of COVID-19

Computer-aided radiological image interpretation systems can be helpful to reshape the overall workflow of the COVID-19 diagnosis process. This article describes an unsupervised CT scan image segmentation approach. This approach begins by performing a morphological reconstruction operation that is useful to remove the effect of the external disturbances on the infected regions and to locate different regions of interest precisely. The optimal size of the structuring element is selected using the Edge Content-based contrast matrix approach. After performing the opening by using the morphological reconstruction operation, further noise is eliminated using the closing-based morphological reconstruction operation. The original pixel space is restored and the obtained image is divided into some non-overlapping smaller blocks and the mean intensity value for each block is computed that is used as the local threshold value for the binarization purpose. It is preferable to manually determine the range of the infected region. If a region is greater than the upper bound then that region will be considered as an exceptional region and processed separately. Three standard metrics MSE, PSNR, and SSIM are used to quantify the outcomes. Both quantitative and qualitative comparisons prove the efficiency and real-life adaptability of this approach. The proposed approach is evaluated with the help of 400 different images and on average, the proposed approach achieves MSE 307.1888625, PSNR 23.7246505, and SSIM 0.831718459. Moreover, the comparative study shows that the proposed approach outperforms some of the standard methods and obtained results are encouraging to support the battle against the COVID-19.