Radiology Residents Research Articles

Radiology resident proficiency in pediatric trauma cases: a comparative analysis based on trauma center status using the WIDI SIM exam.

To investigate the correlation between pediatric and adult trauma center status and radiology resident performance on pediatric trauma cases using the WIDI SIM exam. The WIDI SIM is a validated computer-aided simulation that assesses radiology residents' preparedness for independent call duty. It includes 65 cases across various imaging modalities derived from actual patient encounters. Faculty score free-text responses using a standardized 0-10 scale rubric. We analyzed 8,488 WIDI SIM exam scores from 35 pediatric trauma cases across 52 programs. A negative binomial regression model adjusting for resident level, imaging modality, and case specialty was employed to evaluate the impact of pediatric and adult trauma center status on resident performance. Both pediatric trauma center status (p = 0.0005) and adult trauma center status (p = 0.0003) were significant predictors of higher resident scores. Resident level was also significant, with higher-level residents performing better than first-year residents (p < 0.001). Residents performed worse on MR and US modalities compared to CT, and performance varied by case specialty. Radiology residents' pediatric trauma imaging skills are significantly linked to pediatric and adult trauma center status. Given most pediatric traumas occur outside pediatric trauma centers, targeted training strategies should be considered to ensure residents develop essential diagnostic skills.

Different Evaluation Strategies of Oncological CT Examinations with Regard to Professional Experience: A Clinical Study Using Eye-tracking.

Contrast-enhanced CT is the standard imaging technique in oncological objectives. Rates of missed pathologies depend on work experience of the respective radiologists. Thus the aim of this study is to analyze the eye movements of professionals while reading CT images in order to evaluate whether the eye-fixation patterns and search strategies of experienced radiologists could explain higher detection rates of pathologies and whether such patterns can be learned.Anonymized images of 10 patients were presented to three medical students and six radiologists with different levels of work experience. During image analysis, ocular fixation positions were recorded using an eye-tracking software tool. The CT scans were analyzed retrospectively, considering the individual course of disease with the issue of successful detection of all pathologies. Visual attention and dwell time of ocular fixation on clinically important abnormalities or areas with pathological findings, general search patterns, and time efficiency were assessed. For statistical analysis, interobserver variability and accuracy of lesion detection were evaluated taking into account individual experience.The results revealed that observer sensitivity depends on work experience due to a more systematic order of inspection and a well-known course of disease, e.g. in case of metastatic spread. The areas of missed pathologies mostly included secondary findings. Inexperienced readers changed the stratification considerably more often and required more time for reporting or detecting pathologies.Our results suggest that experienced radiological physicians reduce their amount of missed findings by looking more systematically at images and by applying a more targeted inspection of clinically important regions. · CT interpretation by radiology residents is faster and less error-prone compared to postgraduate residents. · systematic image analysis is trainable. · engrams tend to be acquired through experience. · Kluge S, Schülke C, Ites HC et al. Different Evaluation Strategies of Oncological CT Examinations with Regard to Professional Experience: A Clinical Study Using Eye-tracking. Fortschr Röntgenstr 2024; DOI 10.1055/a-2452-2180.

Association between a healthy lifestyle and the prevalence of depression in radiology residents in China: findings from a nationwide survey

ObjectiveThe relationship between a healthy lifestyle and depression in radiology residents remains poorly understood. This study aims to investigate the relationship between a healthy lifestyle and depressive symptoms in radiology...

Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.

Assessment of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. We evaluate whether assessment of new, expanding, and contrast-enhancing MS lesions can be done more time-efficiently by radiologists with assistance of artificial intelligence (AI). Baseline and three follow-up (FU) MRIs of thirty-five consecutive patients diagnosed with MS were assessed by a radiologist manually, and with assistance of an AI-tool. Results were discussed with a consultant neuroradiologist and time metrics were evaluated. The mean reading time for the resident radiologist was 9.05min (95CI: 6.85-11:25). With AI-assistance, the reading time was reduced by 2.83min (95CI: 3.28-2.41, p < 0.001). The reading decreased steadily from baseline to FU3 for the resident radiologist (9.85min baseline, 9.21 FU1, 8.64 FU2 and 8.44 FU3, p < 0.001). Assistance of AI further remarkably decreased reading times during follow-ups (3.29min FU1, 3.92 FU2, 3.79 FU3, p < 0.001) but not at baseline (0.26min, p = 0.96). The baseline reading time of the resident radiologist was 5.04min (p < 0.001), with each lesion adding 0.14min (p < 0.001). There was a substantial decrease in the baseline reading time from 5.04min to 1.59min (p = 0.23) with AI-assistance. Discussion of the reading results of the resident with the neuroradiology consultant (as usual in clinical routine) was exemplary done for FU-3 MRIs and added another 3min (CI:2.27-3.76) to the reading time without AI-assistance. We found that AI-assisted reading of MRIs of patients with MS may be faster than evaluating these MRIs without AI-assistance.

The price of pressure: nationwide survey on lifestyle disturbances, occupational burnout and compromised perceived-competency among radiology residents in China.

The competency of radiology directly affects the quality and equity of medical services. Due to their different occupational characteristics compared to other specialists, this study aims to evaluate the impacts of lifestyles on competency and burnout in radiology residents in China. A nationwide, cross-sectional survey was conducted from December 1, 2020 to April 30, 2021. A total of 12,208 radiology residents during their standardized residency training in China were invited. Multivariate linear regression and logistic regression were conducted to identify perceived competency and burnout associated with lifestyles. Of the 3,666 participants, 58.02% were female, 82.24% were aged <30 years, 40.53% were from the Eastern region, and 92.06% obtained a bachelor's degree. The radiology residents with high-level lifestyles had higher competency (β = 0.16, 95% CI = [0.01, 0.32]), particularly in the realms of sleep, physical activity, and alcohol consumption. The correlation was stronger in residents with longer work hours and more night shifts. Residents with insomnia (OR = 7.69, 95% CI = [4.23, 14.67]) and less exercise (OR = 6.24, 95% CI = [1.33, 29.37]) had higher burnout risk, while residents who smoked had a lower risk (OR = 0.60, 95% CI = [0.40, 0.89]). And lifestyle factors had a slightly different impact on emotional exhaustion and depersonalization. Radiology residents' lifestyles can be emphasized, as it may reflect their pressure and wellbeing and influence their concentration, competency, burnout and performance. Policymakers and hospital administrators should incorporate practical and modifiable strategies into work routines to improve the lifestyle quality of residents.

Diagnostic value of routine CT perfusion imaging for radiology residents

To evaluate whether incorporating CT perfusion imaging can significantly enhance diagnostic CT accuracy in stroke detection. Two 3rd-year residents (3rd of 5 years of residency) reviewed CT scans of 200 patients with suspected stroke, consisting of 104 patients with a proven stroke and a control group with 96 patients. They analyzed each patient in a blinded and randomized manner in two runs. In one session, they had only non-contrast CT and CT angiography available for diagnosis; in the other session at a later time point, an additional CT perfusion imaging was available. The performance achieved by the two readers was determined in terms of AUC (area under the curve), accuracy, sensitivity, specificity, positive and negative predictive value and Cohen’s Kappa. Reader 1 achieved an AUC of 87.64% with the basic stroke-protocol vs. an AUC of 97.4% with an additional CT-perfusion given. Based on the DeLong test, these values differ significantly (p-value: 0.00017). Reader 2 achieved an AUC of 91.23% in basic stroke-protocol vs. an AUC of 96.42% with an additional CT-perfusion. These values also differ significantly (p-value: 0.02612).. The performance gain achieved with CT-perfusion is most evident in the decrease in the number of false classified cases (Reader 1: 24 to 5; Reader 2: 18 or 14 to 7) and the significant increase in Cohen’s kappa. Our study shows that additional CT-perfusion imaging in stroke diagnosis significantly improves the diagnostic reliability of residents. Therefore, it should be further investigated whether perfusion imaging should be a general standard of initial stroke diagnosis no matter of the onset.

Facial trauma education in radiology: using surgeon feedback as the benchmark for success.

Interpreting CT studies of facial trauma is challenging, and there are often substantial differences in the characterization of complex facial trauma between radiologists and surgeons. We designed a collaborative multidisciplinary project to reconcile differences in facial fracture interpretation through an educational intervention. The effectiveness of this intervention was evaluated through surgeon feedback on radiology reports. Radiology residents, neuroradiology fellows, and neuroradiology attendings were recruited as participants at a single tertiary care academic center. Otolaryngology residents were recruited as evaluators. Participants completed surveys and provided preliminary reports for example cases of facial trauma before and after attending an educational session. Evaluators performed a blinded review of these preliminary reports based on ideal reports developed by surgical and neuroradiology attendings. 26 participants (20 residents, 1 neuroradiology fellow, 5 neuroradiology attendings) completed the study. Six otolaryngology residents participated as evaluators. To assess interrater reliability, three evaluators graded a shared set of 15 reports and demonstrated substantial agreement with a Kendall's W of 0.71. Participants demonstrated significant improvement in overall report accuracy, clarity, and organization. In subunit analysis, there were significant improvements in reporting Le Fort, nasoseptal, and nasoorbitoethmoid fractures. No significant improvements occurred in the reporting of upper face, zygomaticomaxillary complex, or mandibular fractures. In contrast, survey analysis demonstrated significantly improved confidence in interpreting trauma involving all facial subunits. Compared with survey results, surgeon assessment of radiology reports better demonstrated areas of improvement after an educational intervention. A multidisciplinary approach to assessing educational efforts may better evaluate the practical effectiveness of educational interventions.

“3 + X D” structured report in radiology standardized resident training: Can it meet high-level teaching objectives?

PurposeTo evaluate the impact of the innovative “3 + X D” structured report (SR) designed based on Bloom’s taxonomy on the learning outcomes of radiology residents during standardized training. MethodsThis is a prospective study that recruited 120 radiology residents from our hospital between 2020 and 2022. Randomly selected 60 residents from the 2020 grade to constituted the control group, and randomly selected 60 residents from the 2021 grade to formed the experimental group. The former group was trained utilizing the Free-text Reports (FTR) template, while the latter group received training with the “3 + X D” structured reports (SR) template. The learning outcomes of both groups was evaluated utilizing both objective and subjective measures. Objective assessments encompassed examinations of theoretical knowledge, diagnostic skills, and total scores, aligning with the cognitive domains of remembering, understanding, applying, and analyzing as outlined by Bloom’s Taxonomy. Subjective assessments, on the other hand, comprised survey questionnaires administered to residents and feedback from clinical instructors, which correlated with the higher-order cognitive level of analyzing, evaluating, and creating within Bloom’s Taxonomy. ResultsOn 60 residents (mean age, 24.15 years ± 2.11[SD]; 25 male) from control group, and 60 residents (mean age, 24.58 years ± 1.88 [SD]; 27 male) from experimental group. Following the training, significant improvements were observed in the theoretical knowledge, diagnostic skills, and total scores for both groups (p < 0.001). Furthermore, the experimental group demonstrated significantly higher diagnostic skills and total scores compared to the control group (p < 0.001). However, no significant difference was observed in the theoretical knowledge exam between the two groups (p = 0.236). The questionnaire used for subjective assessments had good reliability (Cronbach α was 0.826) and acceptable validity (The KMO was 0.692). Additionally, the survey questionnaires indicated that the experimental group rated higher than the control group in terms of cultivating imaging thinking ability, diagnostic confidence, diagnostic speed, and the convenience of the templates (p < 0.001). Clinicians’ feedback scores for the experimental group markedly surpassed those for the control group (p < 0.05). ConclusionsUtilizing the “3 + X D” SR template grounded in Bloom’s taxonomy for training, the professional competency of radiology residents, particularly their diagnostic skills, saw a marked enhancement, successfully meeting the higher-level educational objectives. Consequently, the “3 + X D” SR template is highly recommended for the standardized training of radiology residents.

Diagnostic accuracy and added value of dynamic chest radiography in detecting pulmonary embolism: A retrospective study

PurposeThis study aimed to assess the diagnostic performance of dynamic chest radiography (DCR) and investigate its added value to chest radiography (CR) in detecting pulmonary embolism (PE). MethodsOf 775 patients who underwent CR and DCR in our hospital between June 2020 and August 2022, individuals who also underwent contrast-enhanced CT (CECT) of the chest within 72 h were included in this study. PE or non-PE diagnosis was confirmed by CECT and the subsequent clinical course. The enrolled patients were randomized into two groups. Six observers, including two thoracic radiologists, two cardiologists, and two radiology residents, interpreted each chest radiograph with and without DCR using a crossover design with a washout period. Diagnostic performance was compared between CR with and without DCR in the standing and supine positions. ResultsSixty patients (15 PE, 45 non-PE) were retrospectively enrolled. The addition of DCR to CR significantly improved the sensitivity, specificity, accuracy, and area under the curve (AUC) in the standing (35.6–70.0 % [P < 0.0001], 84.8–93.3 % [P = 0.0010], 72.5–87.5 % [P < 0.0001], and 0.66–0.85 [P < 0.0001], respectively) and supine (33.3–65.6 % [P < 0.0001], 78.5–92.2 % [P < 0.0001], 67.2–85.6 % [P < 0.0001], and 0.62–0.80 [P = 0.0002], respectively) positions for PE detection. No significant differences were found between the AUC values of DCR with CR in the standing and supine positions (P = 0.11) or among radiologists, cardiologists, and radiology residents (P = 0.14–0.68). ConclusionsIncorporating DCR with CR demonstrated moderate sensitivity, high specificity, and high accuracy in detecting PE, all of which were significantly higher than those achieved with CR alone, regardless of scan position, observer expertise, or experience.

Work stress and competency among radiology residents: the mediating effect of resilience.

Competency of health professionals stands as a fundamental element in ensuring the quality of care. Notably, work stress and resilience are found to be associated with competency of health professionals. However, the role of resilience between work stress and competency remains unexplored. This study aims to investigate competency, work stress and resilience of radiology residents, and to examine the mediating effect of resilience. A cross-sectional survey was conducted among 3,666 radiology residents from 31 provinces of China in 2021. The diagnostic radiology milestones were used to measure the competency. Results of work stress and resilience were derived from self-reports and assessment of the Connor Davidson Resilience Scale (CD-RISC) respectively. To examine the association between work stress, resilience, and competency, Spearman correlation analysis and hierarchical linear regression were employed. The mediating effect of resilience was tested by bootstrapping analysis. Descriptive statistics show that the average score (mean ± SD) of work stress, competency and resilience among radiology residents were 1.55 ± 1.53 (range from 0 to 6), 28.14 ± 13 (range from 0 to 81) and 5.97 ± 1.92 (range from 0 to 8), respectively. Work stress was negatively associated with competency (β = -0.51, p < 0.001) and resilience (-0.57, p < 0.001). In particular, resilience mediated the relationship between work stress and competence, and the total mediating effect was -0.49 (= - 0.57 * 0.86), accounting for 49.06% of the total effect. Work stress is a significant contributor to competence among radiology residents. Resilience significantly mediated the association. This study highlights incorporating resilience training courses into the standardized residency training program to reduce intensive work stress and promote competency of radiology residents.

Challenges in diagnosis of calcaneal fractures: an examination using the WIDI SIM platform.

The calcaneus is the most commonly fractured tarsal bone. Diagnosis is often challenging due to subtle radiographic changes and requires timely identification to prevent complications, including subtalar arthritis, neurovascular injury, malunion, osteomyelitis, and compartment syndrome. Treatment varies based on fracture type, with non-surgical methods for non-displaced stress fractures and surgical interventions for displaced or intra-articular fractures. This study utilized the Wisdom in Diagnostic Imaging Simulation (WIDI SIM) platform, an emergency imaging simulation designed to assess radiology resident preparedness for independent call. During an 8-hour simulation, residents were tested on 65 cases across various imaging modalities of varying complexity, including normal studies. A single, unique case of calcaneal fracture was included within the simulation in four separate years of testing. Cases were assessed using a standardized grading rubric by subspecialty radiology faculty, with errors subsequently classified by type. A total of 1279 residents were tested in five separate years on the findings of calcaneal fractures of 5 different patients. Analysis revealed a consistent pattern of missed diagnoses across all training years, primarily attributed to observational errors. There was limited improvement with training progression as all training years exhibited similar average performance levels. Calcaneal fractures pose a diagnostic challenge due to their frequent subtle radiographic findings, especially in stress fractures. Simulation-based evaluations using WIDI SIM highlighted challenges in radiology residents' proficiency in diagnosing calcaneal fractures. Addressing these challenges through targeted education and exposure to diverse cases is essential to improve diagnostic accuracy and reduce complications with calcaneal fractures.

Demographic Differences in the Radiology Residency Match, 2022 to 2024

PurposeRanking preferences by residency programs in the Match could shape the diversity of selective specialties. We investigated demographic characteristics of applicants and matched residents in radiology and other specialties to identify changes in representation. MethodsSurvey data from the National Resident Matching Program were obtained for applicants to radiology (diagnostic radiology, interventional radiology, and combined diagnostic radiology and nuclear medicine) and nonradiology programs in the 2022 to 2024 Main Residency Matches. Demographics among applicants preferring a specialty, and matched residents were compared using χ2 tests. ResultsRadiology had a 73.9% match rate (3,486 of 4,718 applicants). Women represented 29.0% of radiology applicants compared with 52.0% in other specialties. In radiology, only US citizenship had higher representation among matched residents compared with applicants (+4.0%, 95% confidence interval [CI], 2.8%-5.3%) (P = .001). Other demographics were not significantly different between applicants and matched residents in radiology overall. A higher representation of women was observed in matched residents compared with applicants in diagnostic radiology (+2.4%, 95% CI, 0.2%-4.6%) (P = .031) but not interventional radiology (+0.2%, 95% CI, −5.1% to 5.5%) (P = .944). In nonradiology specialties, female gender, nonheterosexual orientation, White race, US citizenship, first-generation medical graduate, and nonurban childhood were associated with higher match rates. ConclusionUS citizenship but not other demographic variables was associated with higher rates of matching into radiology. Women are underrepresented among radiology applicants and have slightly higher match rates in diagnostic radiology but not interventional radiology.

Musculoskeletal imaging fellowship program directors: A study of educational paths, and scholarly engagement in the United States

PurposeThe overall aim of the study is to analyze and provide a comprehensive understanding of the demographics, educational backgrounds, and scholarly activities of musculoskeletal imaging fellowship program directors across the United States. MethodsA list of all members of the Society of Skeletal Radiology was obtained and musculoskeletal imaging fellowship program directors across the US were included. Publicly available online sources were used to gather demographic and educational information about each musculoskeletal imaging fellowship program director, which included the online curriculum vitae from the program websites, Health Grades, Doximity, and Elsevier's Scopus database. Demographic and educational data including age, gender, educational background (medical school, residency, fellowship), additional degrees, academic rank, prior leadership positions, and metrics of scholarly activity were recorded. Fellowships in diagnostic musculoskeletal radiology along with additional degrees were recorded. A two-tailed unpaired t-test was used to calculate the difference between means of scholarly activity between male and female PDs. ResultsIn this study encompassing 92 programs across the United States, the majority (88) were dedicated to pure Musculoskeletal (MSK) Imaging Fellowship, while one each offered combined training in MSK and Body Imaging, MSK, and Emergency Imaging, MSK Sports, and Interventional Spine, and Pediatric MSK Imaging. Program directors were identified for 90 out of 92 programs, revealing a regional distribution of 29 in the South (31.5 %), 24 in the North East (26.1 %), 20 in the Midwest (21.74 %) and 19 in the West (20.65 %). Gender analysis unveiled a predominantly male representation, with 71 male directors compared to 17 female directors, while age ranged from 30 to 70 years, with a mean age of 47.17 ± 7.4 years. Medical school backgrounds predominantly featured MD degrees (80), followed by IMG (7) and DO (4) degrees, with the most common IMG source being India. Faculty positions were mainly distributed among Assistant Professors (35), Associate Professors (32), and Professors (11). Research output metrics showcased a mean of 41.943 publications and an h-index of 10.625. Extra degrees were obtained by 31 directors, with common additions being other fellowships, MBAs, MS degrees, and PhDs. Notably, some directors held previous or current leadership positions, while a few had completed residencies outside of Radiology or pursued fellowships beyond MSK Imaging. ConclusionMusculoskeletal imaging fellowship program directors across the United States are predominantly male, with approximately 8 % having graduated from international medical schools. The most common training pathway for these directors is completing a diagnostic radiology residency followed by a musculoskeletal radiology fellowship. This study highlighted the gender disparity within this leadership group and emphasized the diverse educational backgrounds that contribute to the field of musculoskeletal imaging.

Interrater agreement and reliability of the Bosniak classification for cystic renal masses version 2019

BackgroundThe Bosniak classification for cystic renal masses has undergone refinements since its inception. The 2019 version provides more objective criteria to enhance interrater agreement but needs validation. This study compares the interrater agreement of the 2005 and 2019 Bosniak classifications for cystic renal masses. MethodsForty cystic renal masses identified on computed tomography scans were selected, distributed equally among the five classes of the 2005 Bosniak classification. Eight radiology residents participated in 2 consecutive rating sessions using the 2005 and 2019 versions, respectively, with a 1-month wash-out period in between. Interrater reliability was assessed using Fleiss' κ, and changes in cyst classes between the versions were assessed using the Wilcoxon signed-rank test. ResultsFleiss' κ values for interrater reliability were 0.354 (0.286–0.431) for 2005 and 0.373 (0.292–0.487) for 2019, indicating fair to moderate agreement. A significant decrease in cyst grades was noted using the 2019 version (Z = 3.49, r = 0.55, P < 0.001) among all cysts assessed by residents and only in complex cysts assessed by consultants (Z = 1.907, r = 0.275, P = 0.048). ConclusionInterrater agreement was similar for both classifications, ranging from fair to moderate. The 2019 version increased the proportion of masses downgraded to lower classes. Comprehensive training may enhance reliability and accuracy.

Concordance and Discordance Between Radiology Residents and Consultant Radiologist Interpretation Of CT Brain

OBJECTIVES The primary objective of this study is to assess the degree of concordance and discordance between the interpretations of computed tomography (CT) brain images by resident and consultant radiologists while emphasizing the critical significance of accurate image interpretation for informed clinical decision-making. METHODOLOGY The evaluation of radiology reports for CT Brain interpretation through a prospective analysis at the Radiology Department of Rehman Medical Institute over two years, from 1st October 2020 to 31st October 2022. A total of 198 patients who underwent cranial CT scans were interpreted by residents (R1, R2, R3, R4). Following this, the consultant radiologists reviewed the images and completed their reports. The reports of the residents and the consultant radiologists were then compared, and concordance was achieved when the residents’ reports were consistent with the final radiologist’s reports. The data collected were recorded in Microsoft Excel. The statistical analysis was performed using SPSS version 22 (IBM Corp., Armonk, NY), and the kappa coefficient was used to determine the level of agreement between residents and consultants.RESULTSAmong the 198 CT Head reports evaluated, 186 of them were in agreement with the final report of the consultant radiologist. Of the correctly diagnosed cases, R1 correctly diagnosed 46 cases, R2 correctly diagnosed 80 cases, R3 correctly diagnosed 54 cases, and R4 correctly diagnosed 6 cases. Our study achieved a percentage agreement of 93.93, with a Cohen's kappa coefficient of 0.8.CONCLUSIONThe overall concordance rate between residents and consultant radiologists was 93.93%, with a kappa coefficient 0.8. This high kappa coefficient indicates strong agreement between the two groups.

Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels

The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents’ mean detection rate increased significantly from 64 to 77% with AI assist, while seniors’ detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67–77% vs. 80–86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates.

Attitude of Students and Professors Toward Quality of Residency Medical Education at the Selected University of Medical Sciences in Tehran in 2023

Background: Clinical education is one of the most essential components of medical student education, as it significantly impacts students' professional futures and the training of healthcare experts. Objectives: This study aimed to assess the quality of clinical teaching from the perspective of residents and professors at selected medical schools in Tehran city. Methods: A descriptive cross-sectional study was conducted using the census technique on 81 residents and 48 faculty members from internal medicine, surgery, radiology, anesthesia, and physical medicine in 2023. The data collection instrument was a reliable and valid questionnaire. After gathering the data, SPSS software computed the score for each item and reported it as a percentage. Results: The research findings showed that residents reported morning reports are always held in 94% of the internal medicine group and 60% of the surgical group. Additionally, 80% of the residents and 100% of the professors in the surgery department stated that the group held regular mortality and morbidity meetings. Thirty-three percent of radiology and physical medicine residents mentioned that the hospital amenities are of good quality. The residents of the physical medicine department reported that theory classes are always held 83% of the time. Conclusions: According to the present findings, the quality of education in internal medicine and surgical courses is optimal from the perspective of both professors and students. Periodic studies on the sustainability and improvement of current educational conditions are recommended.

Screening Patient Misidentification Errors Using a Deep Learning Model of Chest Radiography: A Seven Reader Study.

We aimed to evaluate the ability of deep learning (DL) models to identify patients from a paired chest radiograph (CXR) and compare their performance with that of human experts. In this retrospective study, patient identification DL models were developed using 240,004 CXRs. The models were validated using multiple datasets, namely, internal validation, CheXpert, and Chest ImaGenome (CIG), which include different populations. Model performance was analyzed in terms of disease change status. The performance of the models to identify patients from paired CXRs was compared with three junior radiology residents (group I), two senior radiology residents (group II), and two board-certified expert radiologists (group III). For the reader study, 240 patients (age, 56.617 ± 13.690years, 113 females, 160 same pairs) were evaluated. A one-sided non-inferiority test was performed with a one-sided margin of 0.05. SimChest, our similarity-based DL model, demonstrated the best patient identification performance across multiple datasets, regardless of disease change status (internal validation [area under the receiver operating characteristic curve range: 0.992-0.999], CheXpert [0.933-0.948], and CIG [0.949-0.951]). The radiologists identified patients from the paired CXRs with a mean accuracy of 0.900 (95% confidence interval: 0.852-0.948), with performance increasing with experience (mean accuracy:group I [0.874], group II [0.904], group III [0.935], and SimChest [0.904]). SimChest achieved non-inferior performance compared to the radiologists (P for non-inferiority: 0.015). The findings of this diagnostic study indicate that DL models can screen for patient misidentification using a pair of CXRs non-inferiorly to human experts.

Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Inter-observer consistency on subsolid nodule follow-up recommendation based on National Comprehensive Cancer Network (NCCN) guidelines in low-dose computed tomography (LDCT) lung cancer screening.

Follow-up management of pulmonary nodules is a crucial component of lung cancer screening. Consistency in follow-up recommendations is essential for effective lung cancer screening. This study aimed to assess inter-observer agreement on National Comprehensive Cancer Network (NCCN) guideline-based follow-up recommendation for subsolid nodules from low-dose computed tomography (LDCT) screening. A retrospective collection of LDCT reports from 2014 to 2017 for lung cancer screening was conducted using the Radiology Information System and keyword searches, focusing on subsolid nodules. A total of 110 LDCT cases containing subsolid nodules were identified. Two senior radiologists provided standardized follow-up recommendation. Follow-up recommendation was categorized into four groups (0-, 3-, 6-, and 12-month). To ensure overall balance and representativeness of the follow-up categories, 60 scans from 60 participants were included (distribution ratio 1:1:2:2). Cases were categorised into follow-up recommendation groups by five observers following NCCN guidelines. Fleiss' kappa statistic was used to evaluate inter-observer agreement. Overall accuracy rate for follow-up recommendation among five observers was 72.3%. Chest radiologists' overall agreement was significantly higher than radiology residents (P<0.01). The overall agreement among the five observers was moderate, with a Fleiss' kappa of 0.437. For all paired readers, the mean Cohen's kappa value was 0.603, with 95% confidence interval (CI) from 0.489 to 0.716. Chest radiologists demonstrated substantial agreement, evidenced by a Cohen's kappa of 0.655 (95% CI: 0.503-0.807). In contrast, the mean Cohen's kappa among radiology residents was 0.533 (95% CI: 0.501-0.565). The majority of cases with discrepancies, accounting for 73.5%, were associated with the same risk-dominant nodules. A higher proportion of part-solid nodule was a risk factor for discrepancies. Of the 600 paired readings, major discrepancies and substantial discrepancies were observed in 27.5% and 4.8% (29/600) of the cases. In subsolid nodules, category evaluation of observer follow-up recommendation based on NCCN guidelines achieved moderate consistency. Disagreements were mainly caused by measurement and type disagreements of identical risk-dominant nodules. Part-solid nodule was a contributor for discrepancies in follow-up recommendation. Major and substantial management discrepancies were 27.5% and 4.8% in the paired evaluations.