Radiology of fibrosis part III: genitourinary system.

Fibrosis is a pathological process involving the abnormal deposition of connective tissue, resulting from improper tissue repair in response to sustained injury caused by hypoxia, infection, or physical damage. It can impact any organ, leading to their dysfunction and eventual failure. Additionally, tissue fibrosis plays an important role in carcinogenesis and the progression of cancer.Early and accurate diagnosis of organ fibrosis, coupled with regular surveillance, is essential for timely disease-modifying interventions, ultimately reducing mortality and enhancing quality of life. While extensive research has already been carried out on the topics of aberrant wound healing and fibrogenesis, we lack a thorough understanding of how their relationship reveals itself through modern imaging techniques.This paper focuses on fibrosis of the genito-urinary system, detailing relevant imaging technologies used for its detection and exploring future directions.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance.

Background: Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective: To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods: Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results: AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion: While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. Clinical Impact: Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.

The effect of non-immersive virtual reality radiographic positioning simulation on first-year radiography students' image evaluation performance.

INTRODUCTION: Optimal radiographic image quality is critical because it affects the accuracy of the reporter's interpretation. Radiographers have an ethical obligation to obtain quality diagnostic images while protecting patients from unnecessary radiation, including minimizing rejected and repeated images. Repeated imaging due to positioning errors have increased in recent years. METHODS: This study evaluated the effectiveness of non-immersive virtual reality (VR) simulation on first-year students' evaluation of positioning errors on resultant knee and lumbar spine images. Crossover intervention design was used to deliver radiographic image evaluation instruction through traditional lecture and guided simulation using non-immersive VR to 33 first-year radiography students at a single academic institution located across four geographic program locations. Pre- and post-test knowledge assessments examined participants' ability to recognize positioning errors on multiple-choice and essay questions. RESULTS: Raw mean scores increased on multiple choice questions across the entire cohort for the knee (M = 0.82, SD = 3.38) and lumbar spine (M = 2.91, SD = 3.69) but there was no significant difference in performance by instructional method (p = 0.60). Essay questions reported very minimal to no raw mean score increases for the knee (M = 0.27, SD = 2.78) and lumbar spine (M = 0.00, SD = 2.55), with no significant difference in performance by instructional method (p = 0.72). CONCLUSION: Guided simulation instruction was shown to be as effective as traditional lecture. Results also suggest that novice learners better recognize image evaluation errors and corrections from a list of options but have not yet achieved the level of competence needed to independently evaluate radiographic images for diagnostic criteria. IMPLICATIONS FOR PRACTICE: Non-immersive VR simulation is an effective tool for image evaluation instruction. VR increases access to authentic image evaluation practice by providing a simulated resultant image based off the students' applied positioning skills.

Research designs of publications in radiography professional journals - A modified bibliometric analysis.

INTRODUCTION: Evidence based practice relies on availability of research evidence mostly through peer-reviewed journal publications. No consensus currently exists on the best hierarchy of research evidence, often categorised by the adopted research designs. Analysing the prevalent research designs in radiography professional journals is one vital step in considering an evidence hierarchy specific to the radiography profession and this forms the aim of this study. METHODS: Bibliometric data of publications in three Radiography professional journals within a 10-year period were extracted. The Digital Object Identifier were used to locate papers on publishers' websites and obtain relevant data for analysis. Descriptive analysis using frequencies and percentages were used to represent data while Chi-square was used to analyse relationship between categorical variables. RESULTS: 1830 articles met the pre-set inclusion criteria. Quantitative descriptive studies were the most published design (26.6%) followed by non-RCT experimental studies (18.7%), while Randomised Controlled Trials (RCT) were the least published (1.0%). Systematic reviews (42.9%) showed the highest average percentage increase within the 10-year period, however RCTs showed no net increase. Single-centre studies predominated among experimental studies (RCT = 88.9%; Non-RCT = 95%). Author collaboration across all study designs was notable, with RCTs showing the most (100%). Quantitative and qualitative studies comparatively had similar number of citations when publication numbers were matched. Quantitative descriptive studies had the highest cumulative citations while RCTs had the least. CONCLUSION: There is a case to advocate for more study designs towards the peak of evidence hierarchies such as systematic reviews and RCT. Radiography research should be primarily designed to answer pertinent questions and improve the validity of the profession's evidence base. IMPLICATION FOR PRACTICE: The evidence presented can encourage the adoption of the research designs that enhances radiography profession's evidence base.