Canine companionship as a resilience factor: a quantitative inquiry into the impact of pet ownership on burnout mitigation among radiologists and radiographers.

Background: The demanding nature of diagnostic imaging, coupled with the increasing workload and exposure to high-stress scenarios, underscores the pressing concern of burnout among radiologists and radiographers in modern healthcare settings. The objective was to investigate the interplay between family characteristics, workplace characteristics, pet ownership, and the occurrence of burnout. Methods: An online, quantitative, cross-sectional study with a non-random, purposive sampling method was carried out among Hungarian radiologists and radiographers from 1st of September to 1st of November 2022. Results: We examined the results of 406 responses predominantly from females (79.8%, n = 324), including 70.7% radiographers (n = 287). Cronbach's alpha values for depersonalization (DP), emotional exhaustion (EE), and personal accomplishment (PA) were 0.74, 0.88, and 0.85, respectively. Average burnout scores were 8.35 (SD = 6.62) for DP, 26.26 (SD = 12.74) for EE, and 32.86 (SD = 9.52) for PA. DP demonstrated a balanced distribution (low: 35.7%, moderate: 27.3%, high: 36.9%). Conversely, EE and PA skewed towards high levels, with 52.5% (n = 213) and 49.5% (n = 201). Significant associations were found between gender and DP (p = 0.006), age (31-40 years) and DP/PA (p < 0.001; p = 0.004), absence of children and all burnout dimensions (p < 0.05), and pet ownership (p = 0.004) with lower EE, particularly for dog owners (p = 0.009). Occupation lacked a significant effect on burnout dimensions (p > 0.05). Employees without a second job had higher EE (p = 0.002) and lower PA (p = 0.008). Increasing healthcare experience correlated with decreased DP values (p = 0.001), while working over 40 h weekly negatively impacted all burnout dimensions (p ≤ 0.05). 15.5% (n = 63) exhibited signs of high burnout, with the age group 31-40 demonstrating the highest proportion (25.4%, n = 27) and significant associations with marital status, absence of children, pet ownership, private healthcare, 10-19 years in healthcare, and working over 40 h weekly. Conclusions: There is a pressing need for evidence-based strategies to alleviate burnout among radiologists and radiographers. There is a growing importance of recognizing the role of pets, especially dogs, as valuable companions for emotional support and stress relief. Implementing pet-friendly policies or therapy programs can contribute to a positive and supportive workplace, potentially mitigating burnout among essential frontline healthcare professionals.

Evaluation of the clinical application value of artificial intelligence in diagnosing head and neck aneurysms.

OBJECTIVE: To evaluate the performance of a semi-automated artificial intelligence (AI) software program (CerebralDoc® system) in aneurysm detection and morphological measurement. METHODS: In this study, 354 cases of computed tomographic angiography (CTA) were retrospectively collected in our hospital. Among them, 280 cases were diagnosed with aneurysms by either digital subtraction angiography (DSA) and CTA (DSA group, n = 102), or CTA-only (non-DSA group, n = 178). The presence or absence of aneurysms, as well as their location and related morphological features determined by AI were evaluated using DSA and radiologist findings. Besides, post-processing image quality from AI and radiologists were also rated and compared. RESULTS: In the DSA group, AI achieved a sensitivity of 88.24% and an accuracy of 81.97%, whereas radiologists achieved a sensitivity of 95.10% and an accuracy of 84.43%, using DSA results as the gold standard. The AI in the non-DSA group achieved 81.46% sensitivity and 76.29% accuracy, as per the radiologists' findings. The comparison of position consistency results showed better performance under loose criteria than strict criteria. In terms of morphological characteristics, both the DSA and the non-DSA groups agreed well with the diagnostic results for neck width and maximum diameter, demonstrating excellent ICC reliability exceeding 0.80. The AI-generated images exhibited superior quality compared to the standard software for post-processing, while also demonstrating a significantly reduced processing time. CONCLUSIONS: The AI-based aneurysm detection rate demonstrates a commendable performance, while the extracted morphological parameters exhibit a remarkable consistency with those assessed by radiologists, thereby showcasing significant potential for clinical application.

Evaluating artificial intelligence's role in lung nodule diagnostics: A survey of radiologists in two pilot tertiary hospitals in China.

Objectives: This study assesses the perceptions and attitudes of Chinese radiologists concerning the application of artificial intelligence (AI) in the diagnosis of lung nodules. Material and Methods: An anonymous questionnaire, consisting of 26 questions addressing the usability of AI systems and comprehensive evaluation of AI technology, was distributed to all radiologists affiliated with Beijing Anzhen Hospital and Beijing Tsinghua Changgung Hospital. The data collection was conducted between July 19, and 21, 2023. Results: Of the 90 respondents, the majority favored the AI system's convenience and usability, reflected in "good" system usability scale (SUS) scores (Mean ± standard deviation [SD]: 74.3 ± 11.9). General usability was similarly well-received (Mean ± SD: 76.0 ± 11.5), while learnability was rated as "acceptable" (Mean ± SD: 67.5 ± 26.4). Most radiologists noted increased work efficiency (Mean Likert scale score: 4.6 ± 0.6) and diagnostic accuracy (Mean Likert scale score: 4.2 ± 0.8) with the AI system. Views on AI's future impact on radiology careers varied (Mean ± SD: 3.2 ± 1.4), with a consensus that AI is unlikely to replace radiologists entirely in the foreseeable future (Mean ± SD: 2.5 ± 1.1). Conclusion: Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness. However, the system's learnability requires enhancement. While AI is seen as beneficial for work efficiency and diagnostic accuracy, its long-term career implications remain a topic of debate.

MR enterography radiologic ulcers in newly diagnosed ileal Crohn disease in children: frequency, inter-radiologist agreement, and clinical correlation.

BACKGROUND: Radiologic ulcers are increasingly recognized as an imaging finding of bowel wall active inflammation in Crohn disease (CD). OBJECTIVE: To determine the frequency of ulcers at MR enterography (MRE) in children with newly diagnosed ileal CD, assess agreement between radiologists, and evaluate if their presence correlates with other imaging and clinical features of intestinal active inflammation. MATERIALS AND METHODS: This retrospective study included 108 consecutive pediatric patients (ages 6-18 years) with newly diagnosed ileal CD that underwent clinical MRE prior to treatment initiation between January 2021 and December 2022. MRE examinations were independently reviewed by three pediatric radiologists who indicated the presence vs. absence of ulcers, ulcer severity (categorical depth), and ulcer extent (categorical number of ulcers). Maximum bowel wall thickness and length of disease were measured and averaged across readers. Patient demographics and clinical inflammatory markers were documented from electronic health records. Inter-radiologist agreement was assessed using Fleiss' kappa (k) statistics. Student's t-test was used to compare continuous variables. RESULTS: Mean patient age was 13.9 years (67 [62%] boys). Radiologic ulcers were recorded in 64/108 (59.3%) cases by reader 1, 70/108 (64.8%) cases by reader 2, and 49/108 (45.4%) cases by reader 3 (k = 0.36). Based on majority consensus, radiologic ulcers were present in 60/108 (55.6%) participants. Inter-radiologist agreement for ulcer severity was k = 0.23, while ulcer extent was k = 0.66. There were significant differences in C-reactive protein, erythrocyte sedimentation rate, fecal calprotectin, albumin, maximum bowel wall thickness, and length of disease between patients without and with radiologic ulcers (P < 0.05). The sensitivity and specificity of MRE for detecting endoscopic ulcers were 66.7% (95% CI, 52.1-79.2%) and 69.2% (95% CI, 48.2-85.7%), respectively. CONCLUSION: Radiologic ulcers are visible in children with newly diagnosed ileal CD, although inter-radiologist agreement is only fair. The presence of ulcers is associated with clinical laboratory inflammatory markers as well as other MRE findings of disease activity and is an additional imaging finding that can be used to evaluate intestinal inflammation.

Impact of diffusion-weighted imaging on agreement between radiologists and non-radiologist in musculoskeletal tumor imaging using magnetic resonance.

Background: Diffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors. Aim: This study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist. Methods: Thirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter >4 cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet's AC1 coefficients and interpreted according to Landis and Koch. Results: The lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772-0.965 among only radiologists indicating substantial to almost perfect agreement. Conclusion: The agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.

Testicular torsion and ultrasound-assisted manual detorsion.

Testicular torsion is a medical emergency that requires an immediate and multidisciplinary approach from emergency, surgical, and radiological services. In this article, we discuss the current knowledge and growing value of ultrasound (US) for intravaginal testicular torsion diagnosis and our experience with manual testicular detorsion with US assistance. Testicular torsion requires prompt and accurate diagnosis and quick therapeutic action. Technological advances in US equipment and knowledge of this pathology place the radiologist in an excellent position for its diagnosis and management. During the same medical procedure, the radiologist can both confirm the intravaginal testicular torsion and attempt manual testicular detorsion. US-assisted manual testicular detorsion is a non-invasive, simple, quick, safe, and effective manoeuvre that can rapidly restore testicular blood flow, maximising testicular salvage, relieving the patient's symptoms, and facilitating surgery.

ChatGPT and assistive AI in structured radiology reporting: A systematic review.

INTRODUCTION: The rise of transformer-based large language models (LLMs), such as ChatGPT, has captured global attention with recent advancements in artificial intelligence (AI). ChatGPT demonstrates growing potential in structured radiology reporting-a field where AI has traditionally focused on image analysis. METHODS: A comprehensive search of MEDLINE and Embase was conducted from inception through May 2024, and primary studies discussing ChatGPT's role in structured radiology reporting were selected based on their content. RESULTS: Of the 268 articles screened, eight were ultimately included in this review. These articles explored various applications of ChatGPT, such as generating structured reports from unstructured reports, extracting data from free text, generating impressions from radiology findings and creating structured reports from imaging data. All studies demonstrated optimism regarding ChatGPT's potential to aid radiologists, though common critiques included data privacy concerns, reliability, medical errors, and lack of medical-specific training. CONCLUSION: ChatGPT and assistive AI have significant potential to transform radiology reporting, enhancing accuracy and standardization while optimizing healthcare resources. Future developments may involve integrating dynamic few-shot prompting, ChatGPT, and Retrieval Augmented Generation (RAG) into diagnostic workflows. Continued research, development, and ethical oversight are crucial to fully realize AI's potential in radiology.

Assessing the impact of trained Radiologist Assistants in a busy emergency teleradiology practice: a comprehensive evaluation.

PURPOSE: This study aims to study the feasibility and usefulness of trained Radiologist Assistants in a busy emergency teleradiology practice. METHOD: This is a retrospective study over a 21-month period (January 2021 to September 2022). The study analysed archived data from 247118 peer review studies performed by Radiologist Assistants (RAs) out of a total case volume of 828526 and evaluated the rate of discrepancies, the study types commonly noted to have discrepancies, and the severity of errors. These missed findings were brought to the attention of the radiologists for approval and further decision-making. RESULTS: Peer review by RAs was performed on 30% (n = 247118) of the total volume 828526 studies reported, and yielded additional findings including but not limited to fractures (218; 23%), hemorrhage,(94; 10%) pulmonary thromboembolism, (n = 104; 11%), Calculus (n = 75; 8%) lesion (n = 66; 5%), appendicitis(n = 50; 4%) and others. These were brought to the attention of the radiologist, who agreed in 97% (1279 out of 1318) of cases, and communicated the same to the referring facility, with an addended report. CONCLUSION: Trained RAs can provide value to the peer review program of a busy teleradiology practice and decrease errors. This may be useful to meet the ongoing radiologist shortages.

United States newspaper and online media coverage of artificial intelligence and radiology from 1998 to 2023.

OBJECTIVE: To evaluate the frequency and content of media coverage pertaining to artificial intelligence (AI) and radiology in the United States from 1998 to 2023. METHODS: The ProQuest US Newsstream database was queried for print and online articles mentioning AI and radiology published between January 1, 1998, and March 30, 2023. A Boolean search using terms related to radiology and AI was used to retrieve full text and publication information. One of 9 readers with radiology expertise independently reviewed randomly assigned articles using a standardized scoring system. RESULTS: 379 articles met inclusion criteria, of which 290 were unique and 89 were syndicated articles. Most had a positive sentiment (74 %) towards AI, while negative sentiment was far less common (9 %). Frequency of positive sentiment was highest in articles with a focus on AI and radiology (86 %) and lowest in articles focusing on AI and non-medical topics (55 %). The net impact of AI on radiology was most commonly presented as positive (60 %). Benefits of AI were more frequently mentioned (76 %) than potential harms (46 %). Radiologists were interviewed or quoted in less than one-third of all articles. CONCLUSION: Portrayal of the impact of AI on radiology in US media coverage was mostly positive, and advantages of AI were more frequently discussed than potential risks. However, articles with a general non-medical focus were more likely to have a negative sentiment regarding the impact of AI on radiology than articles with a more specific focus on medicine and radiology. Radiologists were infrequently interviewed or quoted in media coverage.

Test-set results can predict participants' development in breast-screen cancer detection: An observational cohort study.

Background and Aim: Test-sets are standardized assessments used to evaluate reader performance in breast screening. Understanding how test-set results affect real-world performance can help refine their use as a quality improvement tool. The aim of this study is to explore if mammographic test-set results could identify breast-screening readers who improved their cancer detection in association with test-set training. Methods: Test-set results of 41 participants were linked to their annual cancer detection rate change in two periods oriented around their first test-set participation year. Correlation tests and a multiple linear regression model investigated the relationship between each metric in the test-set results and the change in detection rates. Additionally, participants were divided based on their improvement status between the two periods, and Mann-Whitney U test was used to determine if the subgroups differed in their test-set metrics. Results: Test-set records indicated multiple significant correlations with the change in breast cancer detection rate: a moderate positive correlation with sensitivity (0.688, p < 0.001), a moderate negative correlation with specificity (-0.528, p < 0.001), and a low to moderate positive correlation with lesion sensitivity (0.469, p = 0.002), and the number of years screen-reading mammograms (0.365, p = 0.02). In addition, the overall regression was statistically significant (F (2,38) = 18.456 p < 0.001), with an R² of 0.493 (adjusted R² = 0.466) based on sensitivity (F = 27.132, p < 0.001) and specificity (F = 9.78, p = 0.003). Subgrouping the cohort based on the change in cancer detection indicated that the improved group is significantly higher in sensitivity (p < 0.001) and lesion sensitivity (p = 0.02) but lower in specificity (p = 0.003). Conclusion: Sensitivity and specificity are the strongest test-set performance measures to predict the change in breast cancer detection in real-world breast screening settings following test-set participation.

Women in Radiology: Challenges and Opportunities.

Improving the status of women in radiology is crucial to better work environments. There is strong evidence in the business world that women leaders improve the workplace by making it more financially viable and by increasing collaboration, job satisfaction, and engagement. Diverse leadership fosters innovation, and women approach problem-solving with unique insights and collaborative styles. Gender diversity in leadership correlates with improved patient outcomes because women leaders prioritize patient-centered care and communication. Women create sustainable, productive work and improve radiology. Women serve as powerful role models, inspiring the next generation of women in radiology and addressing gender disparities. Increasing women leaders in radiology is essential to increase the number of women in radiology. This article summarizes many challenges women face when taking leadership roles: organizational biases prioritizing male viewpoints and marginalizing women's voices and contributions, a lack of role models, a lack of time ("second shift"), a lack of confidence, a lack of interest or perceived benefit, a lack of support, burnout, and previous poor experiences. While systemic issues are difficult to overcome, this article assists in the training and development of women radiologists by offering strategies to enhance job satisfaction and bring new and valuable perspectives to leadership.

Representation of International Medical Graduates Among Diagnostic Radiology Chairs, Neuroradiology Chiefs, and Program Directors.

RATIONALE AND OBJECTIVES: The number of international medical graduates (IMGs) entering radiology residencies and neuroradiology fellowships averaged 9.7% and 20.9% from 2021 to 2023, respectively. We aimed to determine whether IMG graduates are populating leadership roles at a proportionate rate in diagnostic radiology (DR) and neuroradiology. MATERIALS AND METHODS: We surveyed 191 DR program directors, 94 neuroradiology program directors (PDs), 192 chairs of radiology, and 91 directors of neuroradiology inquiring about their original citizenship and medical school (American Medical Graduates [AMG] vs IMG). We reviewed institutional websites to obtain missing data and recorded H indices for each person using Scopus. RESULTS: We confirmed the original citizenship and medical school location in 61-75% and 93-98% of each leadership group. We found that 16.2% of DR program directors, 43.7% of neuroradiology PDs, 28.5% of Chairs, and 40.6% of neuroradiology directors were not originally US citizens. The IMG rate was 18/188 (9.6%), 20/90 (22.2%), 26/186 (14.0%), and 19/85 (22.4%) for the same groups respectively. The most common country of origin and medical school cited was India for all leadership groups. IMGs had a median H index of 14 while AMG 10, significantly different (p = 0.021) CONCLUSION: Compared to the rate of diagnostic and neuroradiology trainees entering from 2021 to 2023, IMGs are proportionately represented at the leadership positions studied. The H index of the IMGs was higher than AMG. We conclude that IMGs have made substantial and proportionate inroads in radiology and neuroradiology leadership.

Norwegian general practitioners' and radiologists' perspectives on the referral, justification, and unnecessary imaging-a survey.

AIM: This study aimed to survey general practitioners' (GPs) and radiologists' perspectives on referrals, imaging justification, and unnecessary imaging in Norway. MATERIALS AND METHODS: The survey covered access to imaging, responsibilities, attitudes toward justification assessment, referral process, and demographics using multiple choice questions, statements to report agreement with using the Likert scale and one open question. RESULTS: Forty radiologists and 58 GPs attending national conferences completed a web-based survey, with a 20/15% response rate, respectively. Both radiologists (97%) and GPs (100%) considered avoiding unnecessary examinations essential to their role in the healthcare service. Still, 91% of GPs admitted that they referred to imaging they thought was not helpful, while about 60% of the radiologists agreed that unnecessary imaging was conducted in their workplace. GPs reported pressure from patients and patients having private insurance as the most common reasons for doing unnecessary examinations. In contrast, radiologists reported a lack of clinical information and the inability to discuss patient cases with the GPs as the most common reasons. CONCLUSION: This study adds to our understanding of radiologists' and GPs' perspectives on unnecessary imaging and referrals. Better guidelines and, even more importantly, better communication between the referrer and the radiologist are needed. Addressing these issues can reduce unnecessary imaging and improve the quality and safety of care.

T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study.

OBJECTIVES: To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. METHODS: Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. RESULTS: For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. CONCLUSION: Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. CRITICAL RELEVANCE STATEMENT: Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. KEY POINTS: Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.

Radiographers' insights on the impact of their potential role in image interpretation within a low resource setting.

INTRODUCTION: The global shortage of radiologists has led to a growing concern in medical imaging, prompting the exploration of strategies, such as including radiographers in image interpretation, to mitigate this challenge. However, in low-resource settings, progress in adopting similar approaches has been limited. This study aimed to explore radiographers' perceptions regarding the impact of their potential role in image interpretation within a low-resource setting. METHODS: The study used a qualitative descriptive design and was conducted at two public referral hospitals. Radiographers with at least one year of experience were purposively sampled and interviewed using a semi-structured interview guide after consenting. Data saturation determined the sample size, and content analysis was applied for data analysis. RESULTS: Two themes emerged from fourteen interviews conducted with two male and twelve female radiographers. Theme one revealed the potential for enhanced healthcare delivery through improved diagnostic support, bridging radiologist shortages, career development and fulfilment as positive outcomes of role extension. Theme two revealed possible implementation hurdles including radiographer resistance and reluctance, limited training, lack of professional trust, and legal and ethical challenges. CONCLUSION: Radiographers perceived their potential participation positively, envisioning enhanced healthcare delivery, however, possible challenges like resistance and reluctance of radiographers, limited training, and legal/ethical issues pose hurdles. Addressing these challenges through tailored interventions, including formal education could facilitate successful implementation. Further studies are recommended to explore radiographers' competencies, providing empirical evidence for sustaining and expanding this role extension. IMPLICATION FOR PRACTICE: The study further supports the integration of radiographers into image interpretation with the potential to enhance healthcare delivery, however, implementation challenges in low-resource settings require careful consideration.

Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation.

With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes.

Pre-operative Assessment of Shoulder Pathologies on MRI by a Radiologist and an Orthopaedic Surgeon.

Introduction: Pathologies of the shoulder, i.e. rotator cuff tears and labral injuries are very common. Most patients receive MRI examination prior to surgery. A correct assessment of pathologies is significant for a detailed patient education and planning of surgery. Materials and methods: Sixty-nine patients were identified, who underwent both, a standardised shoulder MRI and following arthroscopic shoulder surgery in our hospital. For this retrospective comparative study, the MRIs were pseudonymised and evaluated separately by an orthopaedic surgeon and a radiologist. A third rater evaluated images and reports of shoulder surgery, which served as positive control. Results of all raters were then compared. The aim was an analysis of agreement rates of diagnostic accuracy of preoperative MRI by a radiologist and an orthopaedic surgeon. Results: The overall agreement with positive control of detecting transmural cuff tears was high (84% and 89%) and lower for partial tears (70-80%). Subscapularis tears were assessed with moderate rates of agreement (60 - 70%) compared to intra-operative findings. Labral pathologies were detected mostly correctly. SLAP lesions and pulley lesions of the LHB were identified with only moderate agreement (66.4% and 57.2%) and had a high inter-rater disagreement. Conclusion: This study demonstrated that tears of the rotator cuff (supraspinatus, infraspinatus) and labral pathologies can be assessed in non-contrast pre-operative shoulder MRI images with a high accuracy. This allows a detailed planning of surgery and aftercare. Pathologies of the subscapularis tendon, SLAP lesions and biceps instabilities are more challenging to detect correctly. There were only small differences between a radiologic and orthopaedic interpretation of the images.