Long radiology workdays reduce detection and accommodation accuracy

Pathologists light level preferences using the microscope-study to guide digital pathology display use

Background

Currently, there is a paucity of guidelines relating to displays used for digital pathology making procurement decisions, and optimal display configuration, challenging.Experience suggests pathologists have personal preferences for brightness when using a conventional microscope which we hypothesized could be used as a predictor for display setup.

Methods

We conducted an online survey across six NHS hospitals, totalling 108 practicing pathologists, to capture brightness adjustment habits on both microscopes and displays.A convenience subsample of respondents was then invited to take part in a practical task to determine microscope brightness and display luminance preferences in the normal working environment. A novel adaptation for a lightmeter was developed to directly measure the light output from the microscope eyepiece.

Results

The survey (response rate 59% n=64) indicates 81% of respondents adjust the brightness on their microscope. In comparison, only 11% report adjusting their digital display. Display adjustments were more likely to be for visual comfort and ambient light compensation rather than for tissue factors, common for microscope adjustments. Part of this discrepancy relates to lack of knowledge of how to adjust displays and lack of guidance on whether this is safe; But, 66% felt that the ability to adjust the light on the display was important.Twenty consultants took part in the practical brightness assessment. Light preferences on the microscope showed no correlation with display preferences, except where a pathologist has a markedly brighter microscope light preference. All of the preferences in this cohort were for a display luminance of <500 cd/m2, with 90% preferring 350 cd/m2 or less. There was no correlation between these preferences and the ambient lighting in the room.

Conclusions

We conclude that microscope preferences can only be used to predict display luminance requirements where the microscope is being used at very high brightness levels. A display capable of a brightness of 500 cd/m2 should be suitable for almost all pathologists with 300 cd/m2 suitable for the majority. Although display luminance is not frequently changed by users, the ability to do so was felt to be important by the majority of respondents.Further work needs to be undertaken to establish the relationship between diagnostic performance, luminance preferences, and ambient lighting levels.

Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network

After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation of the entire scan, obtained by aggregating and unifying voxel-level and patch-level predictions. A total of 164 CBCT scans without mandibular fractures and 171 CBCT scans with mandibular fractures were included in this study. Evaluation of JawFracNet demonstrated a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures. The current study proposes the first benchmark for mandibular fracture detection in CBCT scans. Straightforward replication is promoted by publicly sharing the code and providing access to JawFracNet on grand-challenge.org.

Contrast Reactions and Approaches to Staffing the Contrast Reaction Management Team

RATIONALE AND OBJECTIVES

Managing contrast reactions is critical as contrast reactions can be life-threatening and unpredictable. Institutions need an effective system to handle these events. Currently, there is no standard practice for assigning trainees, radiologists, non-radiologist physicians, or other non-physician providers for management of contrast reaction.

MATERIALS AND METHODS

The Association of Academic Radiologists (AAR) created a task force to address this gap. The AAR task force reviewed existing practices, studied available literature, and consulted experts related to contrast reaction management. The Society of Chairs of Academic Radiology Departments (SCARD) members were surveyed using a questionnaire focused on staffing strategies for contrast reaction management.

RESULTS

The task force found disparities in contrast reactions management across institutions and healthcare providers. There is a lack of standardized protocols for assigning personnel for contrast reaction management.

CONCLUSION

The AAR task force suggests developing standardized protocols for contrast reaction management. The protocols should outline clear roles for different healthcare providers involved in these events.

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

Enhancing Radiology Education With a Case-Based Intro to Radiology on the UF WIDI e-Learning Platform

RATIONALE AND OBJECTIVES

This study explores the implementation and efficacy of an online, interactive, case-based radiology education tool, Wisdom in Diagnostic Imaging (WIDI) Case-Based Intro to Radiology (CBIR). We hypothesize that the WIDI CBIR platform would enhance radiology teaching, foster critical thinking, and provide a comprehensive curriculum in imaging interpretation and utilization.

MATERIALS AND METHODS

A focus group consisting of 1 undergraduate, 7 medical students, 9 physician assistant students, and 3 PhD students participated in this study. We tested 3 different teaching methods: a didactic approach without WIDI, a proctored didactic approach using WIDI, and a flipped classroom approach using WIDI. An online survey was conducted to assess student preference and feedback on these methods and the use of WIDI in their curriculum.

RESULTS

Most students preferred the proctored didactic approach with WIDI. They reported that the platform complemented their curriculum and encouraged critical thinking. The modules covered adequate clinical and imaging details and enhanced their skills in imaging interpretation. Despite the limitations of a small sample size and reliance on self-reported outcomes, this study indicates that the WIDI platform could be integrated into PA and medical school curricula throughout the US, offering a standardized radiology curriculum.

CONCLUSION

The UF WIDI appears to be a promising tool for modernizing radiology education, improving imaging interpretation skills, and enhancing appropriate imaging selection among nonradiologist medical learners. WIDI offers case-based education in imaging use, workflow, search-pattern selection, and interpretation of common radiological findings, potentially bridging the gap in radiology education. Further research and larger studies are required to assess the long-term impact on performance and clinical practice.

Fruit and Vegetable Consumption and the Risk of Bone Fracture: A Grading of Recommendations, Assessment, Development, and Evaluations (GRADE)-Assessed Systematic Review and Dose-Response Meta-Analysis

Researchers have examined the link between consuming fruit and vegetables and the incidence of fractures for many years. Nevertheless, their findings have been unclear. Furthermore, the dose-dependent relationship has not been examined, and the level of certainty in the evidence was not evaluated. We carried out a dose-dependent meta-analysis examining the relation between fruit and vegetables intake and fracture incidence. PubMed, Web of Sciences, and Scopus were searched until April 2023 for cohort studies evaluating the relation between fruit and vegetables and fracture incidence. Summary relative risks (RRs) were computed from complied data by applying random effects analysis. To examine the level of evidence, we utilized the approach called the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE). Ten cohort studies comprising 511,716 individuals were entered. There was a nonsignificant relation between fruit and vegetables, as well as only fruit intake and any fracture risk. In contrast, high versus low analysis presented that vegetables consumption was linked to a 16% decrease in any type of fracture incidence (RR 0.84; 95% confidence interval [CI], 0.75 to 0.95; I 2 = 83.1%; n = 6). Also, per one serving/day (200 g/day) increments in vegetables consumption, there was a 14% decline in the fracture risk (RR 0.86; 95% CI, 0.77 to 0.97; I 2 = 84.7%; n = 5; GRADE = moderate). With moderate certainty, a greater consumption of only vegetables, but not total fruit and vegetables or only fruit, might reduce the risk of fracture. These associations were also evident in dose-response analysis. Large intervention trials are demanded to approve our findings. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Can we revolutionize diagnostic imaging by keeping Pandora's box closed?

Incidental imaging findings are a considerable health problem, because they generally result in low-value and potentially harmful care. Healthcare professionals struggle how to deal with them, because once detected they can usually not be ignored. In this opinion article, we first reflect on current practice, and then propose and discuss a new potential strategy to pre-emptively tackle incidental findings. The core principle of this concept is to keep the proverbial Pandora's box closed, i.e. to not visualize incidental findings, which can be achieved using deep learning algorithms. This concept may have profound implications for diagnostic imaging.

Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction

Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings.

Microwave bone fracture diagnosis using deep neural network

This paper studies the feasibility of a deep neural network (DNN) approach for bone fracture diagnosis based on the non-invasive propagation of radio frequency waves. In contrast to previous "semi-automated" techniques, where X-ray images were used as the network input, in this work, we use S-parameters profiles for DNN training to avoid labeling and data collection problems. Our designed network can simultaneously classify different complex fracture types (normal, transverse, oblique, and comminuted) and estimate the length of the cracks. The proposed system can be used as a portable device in ambulances, retirement houses, and low-income settings for fast preliminary diagnosis in emergency locations when expert radiologists are not available. Using accurate modeling of the human body as well as changing tissue diameters to emulate various anatomical regions, we have created our datasets. Our numerical results show that our design DNN is successfully trained without overfitting. Finally, for the validation of the numerical results, different sets of experiments have been done on the sheep femur bones covered by the liquid phantom. Experimental results demonstrate that fracture types can be correctly classified without using potentially harmful and ionizing X-rays.

Work overload and diagnostic errors in radiology

PURPOSE

To determine the association between workload and diagnostic errors on clinical CT scans.

METHOD

This retrospective study was performed at a tertiary care center and covered the period from January 2020 to March 2023. All clinical CT scans that contained an addendum describing a perceptual error (i.e. failure to detect an important abnormality) in the original report that was issued on office days between 7.30 a.m. and 18.00 p.m., were included. The workload of the involved radiologist on the day of the diagnostic error was calculated in terms of relative value units, and normalized for the known average daily production of each individual radiologist (workloadnormalized). A workloadnormalized of less than 100% indicates relative work underload, while a workloadnormalized of > 100% indicates relative work overload in terms of reported examinations on an individual radiologist's basis.

RESULTS

A total of 49 diagnostic errors were included. Top-five locations of diagnostic errors were lung (n = 8), bone (n = 8), lymph nodes (n = 5), peritoneum (n = 5), and liver (n = 4). Workloadnormalized on the days the diagnostic errors were made was on average 121% (95% confidence interval: 106% to 136%), which was significantly higher than 100% (P = 0.008). There was no significant upward monotonic trend in diagnostic errors over the course of the day (Mann-Kendall tau of 0.005, P = 1.000), and there were no other notable temporal trends either.

CONCLUSIONS

Radiologists appear to have a relative work overload when they make a diagnostic error on CT. Diagnostic errors occurred throughout the entire day, without any increase towards the end of the day.

Improving chest X-ray report generation by leveraging warm starting

Automatically generating a report from a patient's Chest X-rays (CXRs) is a promising solution to reducing clinical workload and improving patient care. However, current CXR report generators-which are predominantly encoder-to-decoder models-lack the diagnostic accuracy to be deployed in a clinical setting. To improve CXR report generation, we investigate warm starting the encoder and decoder with recent open-source computer vision and natural language processing checkpoints, such as the Vision Transformer (ViT) and PubMedBERT. To this end, each checkpoint is evaluated on the MIMIC-CXR and IU X-ray datasets. Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm starting the encoder and decoder, respectively. Compared to the state-of-the-art (M2 Transformer Progressive), CvT2DistilGPT2 attained an improvement of 8.3% for CE F-1, 1.8% for BLEU-4, 1.6% for ROUGE-L, and 1.0% for METEOR. The reports generated by CvT2DistilGPT2 have a higher similarity to radiologist reports than previous approaches. This indicates that leveraging warm starting improves CXR report generation. Code and checkpoints for CvT2DistilGPT2 are available at https://github.com/aehrc/cvt2distilgpt2.

Budget Neutrality and Medicare Physician Fee Schedule Reimbursement Trends for Radiologists, 2005 to 2021

PURPOSE

The Medicare program, by law, must remain budget neutral. Increases in volume or relative value units (RVUs) for individual services necessitate declines in either the conversion factor or assigned RVUs for other services for budget neutrality. This study aimed to assess the contribution of budget neutrality on reimbursement trends per Medicare fee-for-service beneficiary for services provided by radiologists.

METHODS

The study used aggregated 100% of Medicare Part B claims from 2005 to 2021. We computed the percentage change in reimbursement per beneficiary, actual and inflation adjusted, to radiologists. These trends were then adjusted by separately holding constant RVUs per beneficiary and the conversion factor to demonstrate the impact of budget neutrality.

RESULTS

Unadjusted reimbursement to radiologists per beneficiary increased 4.2% between 2005 and 2021, but when adjusted for inflation, it declined 24.9%. Over this period, the conversion factor declined 7.9%. Without this decline, the reimbursement per beneficiary would have been 9 percentage points higher in 2021 compared with actual. RVUs per beneficiary performed by radiologists increased 13.1%. Keeping RVUs per beneficiary at 2005 levels, reimbursement per beneficiary would have been 12.1 percentage points lower than observed in 2021.

CONCLUSIONS

Given budget neutrality, a substantial decline has occurred in inflation-adjusted reimbursement to radiologists per Medicare beneficiary. Decreases due to both inflation and the decline in conversion factor are only partially offset by increased RVUs per beneficiary, meaning more services per patient with less overall pay, an equation likely to heighten access challenges for Medicare beneficiaries and shortages of radiologists.

Enhancing X-ray-Based Wrist Fracture Diagnosis Using HyperColumn-Convolutional Block Attention Module

Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention module (CBAM) to enhance fracture detection in X-ray radiographs. Utilizing the EfficientNet-B0 and DenseNet169 models bolstered by the HyperColumn and the CBAM, distinct improvements in fracture site prediction emerge. Significantly, when HyperColumn and CBAM integration is applied, both DenseNet169 and EfficientNet-B0 showed noteworthy accuracy improvements, with increases of approximately 0.69% and 0.70%, respectively. The HyperColumn-CBAM-DenseNet169 model particularly stood out, registering an uplift in the AUC score from 0.8778 to 0.9145. The incorporation of Grad-CAM technology refined the heatmap's focus, achieving alignment with expert-recognized fracture sites and alleviating the deep-learning challenge of heavy reliance on bounding box annotations. This innovative approach signifies potential strides in streamlining training processes and augmenting diagnostic precision in fracture detection.

Factors Associated With Neuroradiologic Diagnostic Errors at a Large Tertiary-Care Academic Medical Center: A Case-Control Study

BACKGROUND. Numerous studies have explored factors associated with diagnostic errors in neuroradiology; however, large-scale multivariable analyses are lacking. OBJECTIVE. The purpose of this study was to evaluate associations of interpretation time, shift volume, care setting, day of week, and trainee participation with diagnostic errors by neuroradiologists at a large academic medical center. METHODS. This retrospective case-control study using a large tertiary-care academic medical center's neuroradiology quality assurance database evaluated CT and MRI examinations for which neuroradiologists had assigned RADPEER scores. The database was searched from January 2014 through March 2020 for examinations without (RADPEER score of 1) or with (RADPEER scores of 2a, 2b, 3a, 3b, or 4) diagnostic error. For each examination with error, two examinations without error were randomly selected (unless only one examination could be identified) and matched by interpreting radiologist and examination type to form case and control groups. Marginal mixed-effects logistic regression models were used to assess associations of diagnostic error with interpretation time (number of minutes since the immediately preceding report's completion), shift volume (number of examinations interpreted during the shift), emergency/inpatient setting, weekend interpretation, and trainee participation in interpretation. RESULTS. The case group included 564 examinations in 564 patients (mean age, 50.0 ± 25.0 [SD] years; 309 men, 255 women); the control group included 1019 examinations in 1019 patients (mean age, 52.5 ± 23.2 years; 540 men, 479 women). In the case versus control group, mean interpretation time was 16.3 ± 17.2 [SD] minutes versus 14.8 ± 16.7 minutes; mean shift volume was 50.0 ± 22.1 [SD] examinations versus 45.4 ± 22.9 examinations. In univariable models, diagnostic error was associated with shift volume (OR = 1.22, p < .001) and weekend interpretation (OR = 1.60, p < .001) but not interpretation time, emergency/inpatient setting, or trainee participation (p > .05). However, in multivariable models, diagnostic error was independently associated with interpretation time (OR = 1.18, p = .003), shift volume (OR = 1.27, p < .001), and weekend interpretation (OR = 1.69, p = .02). In subanalysis, diagnostic error showed independent associations on weekdays with interpretation time (OR = 1.18, p = .003) and shift volume (OR = 1.27, p < .001); such associations were not observed on weekends (interpretation time: p = .62; shift volume: p = .58). CONCLUSION. Diagnostic errors in neuroradiology were associated with longer interpretation times, higher shift volumes, and weekend interpretation. CLINICAL IMPACT. These findings should be considered when designing work-flow-related interventions seeking to reduce neuroradiology interpretation errors.

Oblique Axis Rib Stretch and Curved Planar Reformats in Patients for Rib Fracture Detection and Characterization: Feasibility and Clinical Application

Objective

To assess the use of CT with oblique axis rib stretch (OARS) and curved planar reformats (CPRs) for rib fracture detection and characterization.

Methods

A total of 108 forensically diagnosed patients with rib fractures were evaluated retrospectively. OARS and CPRs were independently used during the diagnosis in two groups. In each group, the final diagnosis was made after a junior radiologist's initial diagnosis was reviewed by a senior radiologist. The images were evaluated for the presence and characterization of rib fractures.

Results

A total of 2,592 ribs were analyzed, and 326 fractured ribs and 345 fracture sites were diagnosed using reference standard. Two groups of radiologists identified 331 and 333 fracture sites using the OARS method, 291 and 288 fracture sites using the CPRs method, and 274 fracture sites in forensically diagnosed patients (CR: conventional reconstruction), respectively; and all missed diagnoses were nondisplaced rib fractures. The ROC Az value of OARS1,2 was 0.98, which is higher than CPRs1,2 0.91, and CR 0.90 (all p < 0.01). The Az value for detecting rib fractures using CPRs1,2 and CR has no statistical difference (p = 0.14 and 0.29). More misdiagnosed patients were found using CPRs1,2 (42 and 44 cases) than OARS1,2 (1 and 2 cases) and CR (2 cases). The displaced fracture detection ratio of all methods showed no difference.

Conclusions

Doctors using the OARS method could improve diagnostic performance for detecting rib fractures without the requirement of specialized software and workstation when compared with CPRs.

Impact of Shift Volume on Neuroradiology Diagnostic Errors at a Large Tertiary Academic Center

BACKGROUND AND PURPOSE

Medical errors can result in significant morbidity and mortality. The goal of our study is to evaluate correlation between shift volume and errors made by attending neuroradiologists at an academic medical center, using a large data set.

MATERIALS AND METHODS

CT and MRI reports from our Neuroradiology Quality Assurance database (years 2014 - 2020) were searched for attending physician errors. Data were collected on shift volume, category of missed findings, error type, interpretation setting, exam type, clinical significance.

RESULTS

654 reports contained diagnostic error. There was a significant difference between mean volume of interpreted studies on shifts when an error was made compared with shifts in which no error was documented (46.58 (SD=22.37) vs 34.09 (SD=18.60), p<0.00001); and between shifts when perceptual error was made compared with shifts when interpretive errors were made (49.50 (SD=21.9) vs 43.26 (SD=21.75), p=0.0094). 59.6% of errors occurred in the emergency/inpatient setting, 84% were perceptual and 91.1% clinically significant. Categorical distribution of errors was: vascular 25.8%, brain 23.4%, skull base 13.8%, spine 12.4%, head/neck 11.3%, fractures 10.2%, other 3.1%. Errors were detected most often on brain MRI (25.4%), head CT (18.7%), head/neck CTA (13.8%), spine MRI (13.7%).

CONCLUSION

Errors were associated with higher volume shifts, were primarily perceptual and clinically significant. We need National guidelines establishing a range of what is a safe number of interpreted cross-sectional studies per day.

Work-related musculoskeletal symptoms among Saudi radiologists: a cross-sectional multi-centre study

BACKGROUND

Musculoskeletal disorders are common health problems worldwide. Several factors cause these symptoms, including ergonomics and other individual considerations. Computer users are prone to repetitive strain injuries that increase the risk of developing musculoskeletal symptoms (MSS). Radiologists are susceptible to developing MSS because they work long hours analysing medical images on computers in an increasingly digitalised field. This study aimed to identify the prevalence of MSS among Saudi radiologists and the associated risk factors.

METHODS

This study was a cross-sectional, non-interventional, self-administered online survey. The study was conducted on 814 Saudi radiologists from various regions in Saudi Arabia. The study's outcome was the presence of MSS in any body region that limited participation in routine activities over the previous 12 months. The results were descriptively examined using binary logistic regression analysis to estimate the odds ratio (OR) of participants who had disabling MSS in the previous 12 months. All university, public, and private radiologists received an online survey containing questions about work surroundings, workload (e.g., spent at a computer workstation), and demographic characteristics.

RESULTS

The prevalence of MSS among the radiologists was 87.7%. Most of the participants (82%) were younger than 40 years of age. Radiography and computed tomography were the most common imaging modalities that caused MSS (53.4% and 26.8%, respectively). The most common symptoms were neck pain (59.3%) and lower back pain (57.1%). After adjustment, age, years of experience, and part-time employment were significantly associated with increased MSS (OR = .219, 95% CI = .057-.836; OR = .235, 95% CI = 087-.634; and OR = 2.673, 95% CI = 1.434-4.981, respectively). Women were more likely to report MSS than males (OR = 2.12, 95% CI = 1.327-3.377).

CONCLUSIONS

MSS are common among Saudi radiologists, with neck pain and lower back pain being the most frequently reported symptoms. Gender, age, years of experience, type of imaging modality, and employment status were the most common associated risk factors for developing MSS. These findings are vital for the development of interventional plans to reduce the prevalence of musculoskeletal complaints in clinical radiologists.

Best Practices for Technology in Clinical Social Work and Mental Health Professions to Promote Well-being and Prevent Fatigue

The shift to communication technologies during the pandemic has had positive and negative effects on clinical social worker practice. Best practices are identified for clinical social workers to maintain emotional well-being, prevent fatigue, and avoid burnout when using technology. A scoping review from 2000 to 21 of 15 databases focused on communication technologies for mental health care within four areas: (1) behavioral, cognitive, emotional, and physical impact; (2) individual, clinic, hospital, and system/organizational levels; (3) well-being, burnout, and stress; and (4) clinician technology perceptions. Out of 4795 potential literature references, full text review of 201 papers revealed 37 were related to technology impact on engagement, therapeutic alliance, fatigue and well-being. Studies assessed behavioral (67.5%), emotional (43.2%), cognitive (57.8%), and physical (10.8%) impact at the individual (78.4%), clinic (54.1%), hospital (37.8%) and system/organizational (45.9%) levels. Participants were clinicians, social workers, psychologists, and other providers. Clinicians can build a therapeutic alliance via video, but this requires additional skill, effort, and monitoring. Use of video and electronic health records were associated with clinician physical and emotional problems due to barriers, effort, cognitive demands, and additional workflow steps. Studies also found high user ratings on data quality, accuracy, and processing, but low satisfaction with clerical tasks, effort required and interruptions. Studies have overlooked the impact of justice, equity, diversity and inclusion related to technology, fatigue and well-being, for the populations served and the clinicians providing care. Clinical social workers and health care systems must evaluate the impact of technology in order to support well-being and prevent workload burden, fatigue, and burnout. Multi-level evaluation and clinical, human factor, training/professional development and administrative best practices are suggested.

Changes in Radiologists' Gaze Patterns Against Lung X-rays with Different Abnormalities: a Randomized Experiment

The workload of some radiologists increased dramatically in the last several, which resulted in a potentially reduced quality of diagnosis. It was demonstrated that diagnostic accuracy of radiologists significantly reduces at the end of work shifts. The study aims to investigate how radiologists cover chest X-rays with their gaze in the presence of different chest abnormalities and high workload. We designed a randomized experiment to quantitatively assess how radiologists' image reading patterns change with the radiological workload. Four radiologists read chest X-rays on a radiological workstation equipped with an eye-tracker. The lung fields on the X-rays were automatically segmented with U-Net neural network allowing to measure the lung coverage with radiologists' gaze. The images were randomly split so that each image was shown at a different time to a different radiologist. Regression models were fit to the gaze data to calculate the treads in lung coverage for individual radiologists and chest abnormalities. For the study, a database of 400 chest X-rays with reference diagnoses was assembled. The average lung coverage with gaze ranged from 55 to 65% per radiologist. For every 100 X-rays read, the lung coverage reduced from 1.3 to 7.6% for the different radiologists. The coverage reduction trends were consistent for all abnormalities ranging from 3.4% per 100 X-rays for cardiomegaly to 4.1% per 100 X-rays for atelectasis. The more image radiologists read, the smaller part of the lung fields they cover with the gaze. This pattern is very stable for all abnormality types and is not affected by the exact order the abnormalities are viewed by radiologists. The proposed randomized experiment captured and quantified consistent changes in X-ray reading for different lung abnormalities that occur due to high workload.

Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment

PURPOSE

The purpose of this study was to evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two-dimensional whole-spine radiograph.

MATERIAL AND METHODS

This retrospective observational study included 100 patients (35 men, 65 women) with a median age of 14 years (IQR: 13, 15.25; age range: 3-64 years) who underwent conventional uniplanar two-dimensional whole-spine radiograph in standing position between January and July 2022. Ten most commonly used spinopelvic coronal and sagittal parameters were retrospectively measured without AI by a junior radiologist and approved or adjusted by a senior musculoskeletal radiologist to reach final measurements. Final measurements were used as the ground truth to assess AI performance for each parameter. AI performances were estimated using mean absolute errors (MAE), intraclass correlation coefficient (ICCs), and accuracy for selected clinically relevant thresholds. Readers visually classified AI outputs to assess reliability at a patient-level.

RESULTS

AI solution showed excellent consistency without bias in coronal (ICCs ≥ 0.95; MAE ≤ 2.9° or 1.97 mm) and sagittal (ICCs ≥ 0.85; MAE ≤ 4.4° or 2.7 mm) spinopelvic evaluation, except for kyphosis (ICC = 0.58; MAE = 8.7°). AI accuracy to classify low Cobb angle, severe scoliosis or frontal pelvic asymmetry was 91% (95% CI: 85-96), 99% (95% CI: 97-100) and 94% (95% CI: 89-98), respectively. Overall, AI provided reliable measurements in 72/100 patients (72%).

CONCLUSION

The AI solution used in this study for combined coronal and sagittal spinopelvic balance assessment provides results consistent with those of a senior musculoskeletal radiologist, and shows potential benefit for reducing workload in future routine implementation.

Identification of factors associated with diagnostic performance variation in reporting of mammograms: A review

OBJECTIVES

This narrative review aims to identify what factors are linked to diagnostic performance variation for those who interpret mammograms. Identification of influential factors has potential to contribute to the optimisation of breast cancer diagnosis. PubMed, ScienceDirect and Google Scholar databases were searched using the following terms: 'Radiology', 'Radiologist', 'Radiographer', 'Radiography', 'Mammography', 'Interpret', 'read', 'observe' 'report', 'screen', 'image', 'performance' and 'characteristics.' Exclusion criteria included articles published prior to 2000 as digital mammography was introduced at this time. Non-English articles language were also excluded. 38 of 2542 studies identified were analysed.

KEY FINDINGS

Influencing factors included, new technology, volume of reads, experience and training, availability of prior images, social networking, fatigue and time-of-day of interpretation. Advancements in breast imaging such as digital breast tomosynthesis and volume of mammograms are primary factors that affect performance as well as tiredness, time-of-day when images are interpreted, stages of training and years of experience. Recent studies emphasised the importance of social networking and knowledge sharing if breast cancer diagnosis is to be optimised.

CONCLUSION

It was demonstrated that data on radiologist performance variability is widely available but there is a paucity of data on radiographers who interpret mammographic images.

IMPLICATIONS FOR PRACTICE

This scarcity of research needs to be addressed in order to optimise radiography-led reporting and set baseline values for diagnostic efficacy.

Pediatric hand fractures detection on radiographs: do localization cues improve diagnostic performance?

OBJECTIVE

To compare the diagnostic accuracy and interpretation time for detection of pediatric fractures on hand radiographs with and without localization cues.

MATERIALS AND METHODS

Consecutive children, who underwent radiographic examinations after injury, over 2 years (2019-2021) and with > 2 weeks of follow-up to confirm the presence or absence of a fracture, were included. Four readers, blinded to history and diagnosis, retrospectively reviewed all images twice, without and with cue, at least 1 week apart and after randomization, to determine the presence or absence of a fracture, and if present, anatomic location and diagnostic confidence were recorded. Interpretation time for each study was also recorded and averaged across readers. Inter-reader agreement was calculated using Fleiss' kappa. Diagnostic accuracy and interpretation time were compared between examinations using sensitivity, specificity, and Mann-Whitney U correlation.

RESULTS

Study group included 92 children (61 boys, 31 girls; 10.8 ± 3.4 years) with and 40 (31 boys, 9 girls; 10.9 ± 3.7 years) without fractures. Cue improved inter-reader agreement (κ = 0.47 to 0.62). While the specificity decreased (63 to 62%), sensitivity (75 to 78%), diagnostic accuracy (71 to 73%), and confidence improved (78 to 87%, p < 0.01), and interpretation time (median: 40 to 22 s, p < 0.001) reduced with examinations with localization cue. Specifically, examinations with fracture and cue had the shortest interpretation time (median: 16 s), whereas examinations without fracture and without cue had the longest interpretation time (median: 48 s).

CONCLUSION

Localization cues increased inter-reader agreement and diagnostic confidence, reduced interpretation time in the detection of fractures on pediatric hand radiographs, while maintaining diagnostic accuracy.

Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review

Background and Objective

Vascular calcification has important clinical significance due to its vital prognostic value for cardiovascular diseases, chronic kidney disease (CKD), diabetes, fracture, and other multisystem diseases. Radiology is the main diagnostic method of it, but facing great pressure such as the increasing workload and decreasing working accuracy rate. Therefore, radiology needs to find a way out to better realize the clinical value of vascular calcification. Artificial intelligence (AI) encompasses any algorithm imitating human intelligence. AI has shown great potential in image analysis, such as its high speed and accuracy, becoming the savior of the current situation. In order to promote more rational utilization, the role and progress of AI in this field were reviewed.

Methods

A search was conducted in PubMed and Web of Science. The key words included "artificial intelligence", "machine learning", "deep learning", and "vascular calcification". The qualitative analysis of literature was achieved through repeated deliberation after refining valuable content. The theme is the role and progress of AI in the diagnostic radiology of vascular calcification.

Key Content and Findings

Sixty-two articles were included. AI has been applied to the diagnostic radiology of 5 types of vascular calcification, including coronary artery calcification (CAC), thoracic aortic calcification (TAC), abdominal aortic calcification (AAC), carotid artery calcification, and breast artery calcification (BAC). Deep learning (DL), the latest technology in this field has been well applied and satisfactorily performed. Radiologists have been able to achieve efficient diagnosis of 5 types of vascular calcification through AI, with reliable accuracy.

Conclusions

Increasingly, advanced AI has achieved an accuracy comparable to that of human experts, with a faster speed. Moreover, the ability to reduce noise and artifacts enables more imaging equipment to obtain reliable quantification. AI has acquired the ability to cooperate with radiology departments in future work. However, the research in AAC and carotid artery calcification can be more in-depth, and more types of vascular calcification and more fields of radiology should be expanded to. The interpretation of results made by AI and the promotion of existing achievements to the development of other disciplines are also the focus in future.

Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study

OBJECTIVE

To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department.

METHODS

Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3.

RESULTS

The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p <  0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p >  0.017). Radiologist I's FPS increased significantly in P2 compared to P1 (p <  0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p >  0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p >  0.05).

CONCLUSIONS

DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.

A lightweight convolutional neural network model with receptive field block for C-shaped root canal detection in mandibular second molars

Rapid and accurate detection of a C-shaped root canal on mandibular second molars can assist dentists in diagnosis and treatment. Oral panoramic radiography is one of the most effective methods of determining the root canal of teeth. There are already some traditional methods based on deep learning to learn the characteristics of C-shaped root canal tooth images. However, previous studies have shown that the accuracy of detecting the C-shaped root canal still needs to be improved. And it is not suitable for implementing these network structures with limited hardware resources. In this paper, a new lightweight convolutional neural network is designed, which combined with receptive field block (RFB) for optimizing feature extraction. In order to optimize the hardware resource requirements of the model, a lightweight, multi-branch, convolutional neural network model was developed in this study. To improve the feature extraction ability of the model for C-shaped root canal tooth images, RFB has been merged with this model. RFB has achieved excellent results in target detection and classification. In the multiscale receptive field block, some small convolution kernels are used to replace the large convolution kernels, which allows the model to extract detailed features and reduce the computational complexity. Finally, the accuracy and area under receiver operating characteristics curve (AUC) values of C-shaped root canals on the image data of our mandibular second molars were 0.9838 and 0.996, respectively. The results show that the deep learning model proposed in this paper is more accurate and has lower computational complexity than many other similar studies. In addition, score-weighted class activation maps (Score-CAM) were generated to localize the internal structure that contributed to the predictions.

Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.

Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography

The emergency department is an environment with a potential risk for diagnostic errors during trauma care, particularly for fractures. Convolutional neural network (CNN) deep learning methods are now widely used in medicine because they improve diagnostic accuracy, decrease misinterpretation, and improve efficiency. In this study, we investigated whether automatic localization and classification using CNN could be applied to pelvic, rib, and spine fractures. We also examined whether this fracture detection algorithm could help physicians in fracture diagnosis. A total of 7664 whole-body CT axial slices (chest, abdomen, pelvis) from 200 patients were used. Sensitivity, precision, and F1-score were calculated to evaluate the performance of the CNN model. For the grouped mean values for pelvic, spine, or rib fractures, the sensitivity was 0.786, precision was 0.648, and F1-score was 0.711. Moreover, with CNN model assistance, surgeons showed improved sensitivity for detecting fractures and the time of reading and interpreting CT scans was reduced, especially for less experienced orthopedic surgeons. Application of the CNN model may lead to reductions in missed fractures from whole-body CT images and to faster workflows and improved patient care through efficient diagnosis in polytrauma patients.

Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists' Gaze Patterns

Around 60-80% of radiological errors are attributed to overlooked abnormalities, the rate of which increases at the end of work shifts. In this study, we run an experiment to investigate if artificial intelligence (AI) can assist in detecting radiologists' gaze patterns that correlate with fatigue. A retrospective database of lung X-ray images with the reference diagnoses was used. The X-ray images were acquired from 400 subjects with a mean age of 49 ± 17, and 61% men. Four practicing radiologists read these images while their eye movements were recorded. The radiologists passed a series of concentration tests at prearranged breaks of the experiment. A U-Net neural network was adapted to annotate lung anatomy on X-rays and calculate coverage and information gain features from the radiologists' eye movements over lung fields. The lung coverage, information gain, and eye tracker-based features were compared with the cumulative work done (CDW) label for each radiologist. The gaze-traveled distance, X-ray coverage, and lung coverage statistically significantly (p < 0.01) deteriorated with cumulative work done (CWD) for three out of four radiologists. The reading time and information gain over lungs statistically significantly deteriorated for all four radiologists. We discovered a novel AI-based metric blending reading time, speed, and organ coverage, which can be used to predict changes in the fatigue-related image reading patterns.

Mandating Limits on Workload, Duty, and Speed in Radiology

Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.

CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department

PURPOSE

Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs.

MATERIALS AND METHODS

In this retrospective cohort study, a dataset of 7434 prior chest radiographic studies from 6551 ED patients was used to develop and validate a deep learning model to identify radiographic pneumonia, pleural effusions, and evidence of multilobar pneumonia. Model performance was evaluated against 3 radiologists' adjudicated interpretation and compared with performance of the natural language processing of radiology reports used by ePNa.

RESULTS

The deep learning model achieved an area under the receiver operating characteristic curve of 0.833 (95% confidence interval [CI]: 0.795, 0.868) for detecting radiographic pneumonia, 0.939 (95% CI: 0.911, 0.962) for detecting pleural effusions and 0.847 (95% CI: 0.800, 0.890) for identifying multilobar pneumonia. On all 3 tasks, the model achieved higher agreement with the adjudicated radiologist interpretation compared with ePNa.

CONCLUSIONS

A deep learning model demonstrated higher agreement with radiologists than the ePNa CDSS in detecting radiographic pneumonia and related findings. Incorporating deep learning models into pneumonia CDSS could enhance diagnostic performance and improve pneumonia management.

On-call abdominal ultrasonography: the rate of negative examinations and incidentalomas in a European tertiary care center

Objectives

To determine the proportions of abdominal US examinations during on-call hours that are negative and that contain an incidentaloma, and to explore temporal changes and determinants.

Methods

This study included 1615 US examinations that were done during on-call hours at a tertiary care center between 2005 and 2017.

Results

The total proportion of negative US examinations was 49.2% (795/1615). The total proportion of US examinations with an incidentaloma was 8.0% (130/1615). There were no significant temporal changes in either one of these proportions. The likelihood of a negative US examination was significantly higher when requested by anesthesiology [odds ratio (OR) 2.609, P = 0.011], or when the indication for US was focused on gallbladder and biliary ducts (OR 1.556, P = 0.007), transplant (OR 2.371, P = 0.005), trauma (OR 3.274, P < 0.001), or urolithiasis/postrenal obstruction (OR 3.366, P < 0.001). In contrast, US examinations were significantly less likely to be negative when requested by urology (OR 0.423, P = 0.014), or when the indication for US was acute oncology (OR 0.207, P = 0.045) or appendicitis (OR 0.260, P < 0.001). The likelihood of an incidentaloma on US was significantly higher in older patients (OR 1.020 per year of age increase, P < 0.001) or when the liver was evaluated with US (OR 3.522, P < 0.001).

Discussion

Nearly 50% of abdominal US examinations during on-call hours are negative, and 8% reveal an incidentaloma. Requesting specialty and indication for US affect the likelihood of a negative examination, and higher patient age and liver evaluations increase the chance of detecting an incidentaloma in this setting. These data may potentially be used to improve clinical reasoning and restrain overutilization of imaging.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-022-03525-1.

Evaluation of neuroradiology emergency MRI interpretations: low discrepancy rates between on-call radiology residents' preliminary interpretations and neuroradiologists' final reports

OBJECTIVES

To evaluate the performance of on-call radiology residents in interpreting alone brain and spine MRI studies performed after hours, to describe their mistakes, and to identify influencing factors that increased the occurrence of errors.

METHODS

A total of 328 MRI examinations performed during a 13-month period (from December 1, 2019, to January 1, 2021) were prospectively included. Discrepancies between the preliminary interpretation of on-call radiology residents and the final reports of attending neuroradiologists were noted and classified according to a three-level score: level 1 (perfect interpretation or minor correction), level 2 (important correction without immediate change in patient management), or level 3 (major correction with immediate change in patient management). Categorical data were compared using Fisher's exact test.

RESULTS

The overall discrepancy rate (level-2 and level-3 errors) was 16%; the rate of major discrepancies (only level-3 errors) was 5.5%. The major-discrepancy rate of second-year residents, when compared with that of senior residents, was significantly higher (p = 0.02). Almost all of the level-3 errors concerned cerebrovascular pathology. The most common level-2 errors involved undescribed aneurysms. We found no significant difference in the major-discrepancy rate regarding time since the beginning of the shift.

CONCLUSIONS

The great majority of examinations were correctly interpreted. The rate of major discrepancies in our study was comparable to the data in the literature, and there was no adverse clinical outcome. The level of residency has an effect on the rate of serious errors in residents' reports.

KEY POINTS

• The rate of major discrepancies between preliminary MRI interpretations by on-call radiology residents and final reports by attending neuroradiologists is low, and comparable to discrepancy rates reported for head CT interpretations. • The youngest residents made significantly more serious errors when compared to senior residents. • There was no adverse clinical outcome in patient morbidity as a result of an initial misdiagnosis.

Detection and classification of mandibular fracture on CT scan using deep convolutional neural network

OBJECTIVES

This study aimed to evaluate the accuracy and reliability of convolutional neural networks (CNNs) for the detection and classification of mandibular fracture on spiral computed tomography (CT).

MATERIALS AND METHODS

Between January 2013 and July 2020, 686 patients with mandibular fractures who underwent CT scan were classified and annotated by three experienced maxillofacial surgeons serving as the ground truth. An algorithm including two convolutional neural networks (U-Net and ResNet) was trained, validated, and tested using 222, 56, and 408 CT scans, respectively. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by DICE, accuracy, sensitivity, specificity, and area under the ROC curve (AUC).

RESULTS

One thousand five hundred six mandibular fractures in nine subregions of 686 patients were diagnosed. The DICE of mandible segmentation using U-Net was 0.943. The accuracies of nine subregions were all above 90%, with a mean AUC of 0.956.

CONCLUSIONS

CNNs showed comparable reliability and accuracy in detecting and classifying mandibular fractures on CT.

CLINICAL RELEVANCE

The algorithm for automatic detection and classification of mandibular fractures will help improve diagnostic efficiency and provide expertise to areas with lower medical levels.

PCXRNet: Pneumonia diagnosis from Chest X-Ray Images using Condense attention block and Multiconvolution attention block

Coronavirus disease 2019 (COVID-19) has become a global pandemic. Many recognition approaches based on convolutional neural networks have been proposed for COVID-19 chest X-ray images. However, only a few of them make good use of the potential inter- and intra-relationships of feature maps. Considering the limitation mentioned above, this paper proposes an attention-based convolutional neural network, called PCXRNet, for diagnosis of pneumonia using chest X-ray images. To utilize the information from the channels of the feature maps, we added a novel condense attention module (CDSE) that comprised of two steps: condensation step and squeeze-excitation step. Unlike traditional channel attention modules, CDSE first downsamples the feature map channel by channel to condense the information, followed by the squeeze-excitation step, in which the channel weights are calculated. To make the model pay more attention to informative spatial parts in every feature map, we proposed a multi-convolution spatial attention module (MCSA). It reduces the number of parameters and introduces more nonlinearity. The CDSE and MCSA complement each other in series to tackle the problem of redundancy in feature maps and provide useful information from and between feature maps. We used the ChestXRay2017 dataset to explore the internal structure of PCXRNet, and the proposed network was applied to COVID-19 diagnosis. Additional experiments were conducted on a tuberculosis dataset to verify the effectiveness of PCXRNet. As a result, the network achieves an accuracy of 94.619%, recall of 94.753%, precision of 95.286%, and F1-score of 94.996% on the COVID-19 dataset.

Digital Breast Tomosynthesis and Digital Mammography Recall and False-Positive Rates by Time of Day and Reader Experience

Background Digital breast tomosynthesis (DBT) image interpretation might be more cognitively demanding than interpretation of digital mammography (DM) images. The time of day of interpretation might affect recall and false-positive (FP) rates, especially for DBT. Purpose To determine whether recall and FP rates vary by time of day of interpretation for screening mammography for breast cancer performed with DM and DBT. Materials and Methods This is a retrospective study examining 97 671 screening mammograms interpreted by 18 radiologists between January 2018 and December 2019 at one of 12 community radiology sites. The association between the time of day of interpretation, the type of image interpreted (DM vs DBT), and radiologist experience (≤5 posttraining years vs >5 posttraining years) and the likelihood of a patient being recalled from screening mammography and the likelihood of whether the interpretation was FP or true positive were analyzed. Analyses were conducted using generalized linear mixed modeling with a binary distribution and sandwich estimation where observations were nested by radiologist. Results Screening mammograms interpreted by 18 radiologists were reviewed (40 220 DBTs, 57 451 DMs). Nine radiologists had 5 or fewer posttraining years of experience, and nine had more than 5 posttraining years of experience. The overall recall rates for DM (10.2%) and DBT (9.0%) were different (P = .006); FP rate also differed (9.8% DM, 8.6% DBT; P = .004). For radiologists with 5 or fewer posttraining years of experience, odds of recall increased 11.5% (odds ratio [OR] = 1.12, P = .01) with every hour when using DBT, but this was not found for DM (OR = 1.09, P = .06); DBT and DM were different (OR = 1.12 vs 1.09, P = .02). For radiologists with more than 5 posttraining years of experience, no evidence of increase in recall was observed for DBT (OR = 1.02, P = .27) or DM (OR = 1.0, P = .80), and there was no evidence that these were different (OR = 1.02 vs 1.0, P = .13). Conclusion Patients were more likely to be recalled when their screening digital breast tomosynthesis images were interpreted later in the day by less-experienced radiologists. © RSNA, 2022 Online supplemental material is available for this article.

Variations in breast cancer detection rates during mammogram-reading sessions: does experience have an impact?

OBJECTIVES

To examine whether radiologists' performances are consistent throughout a reading session and whether any changes in performance over the reading task differ depending on experience of the reader.

METHODS

The performance of ten radiologists reading a test set of 60 mammographic cases without breaks was assessed using an ANOVA, 2 × 3 factorial design. Participants were categorized as more (≥2,000 mammogram readings per year) or less (<2,000 readings per year) experienced. Three series of 20 cases were chosen to ensure comparable difficulty and presented in the same sequence to all readers. It usually takes around 30 min for a radiologist to complete each of the 20-case series, resulting in a total of 90 min for the 60 mammographic cases. The sensitivity, specificity, lesion sensitivity, and area under the ROC curve were calculated for each series. We hypothesized that the order in which a series was read (i.e. fixed-series sequence) would have a significant main effect on the participants' performance. We also determined if significant interactions exist between the fixed-series sequence and radiologist experience.

RESULTS

Significant linear interactions were found between experience and the fixed sequence of the series for sensitivity (F[1] =5.762, p = .04, partial η² = .41) and lesion sensitivity. (F[1] =6.993, p = .03, partial η² = .46). The two groups' mean scores were similar for the first series but progressively diverged. By the end of the third series, significant differences in sensitivity and lesion sensitivity were evident, with the more experienced individuals demonstrating improving and the less experienced declining performance. Neither experience nor series sequence significantly affected the specificity or the area under the ROC curve.

CONCLUSIONS

Radiologists' performance may change considerably during a reading session, apparently as a function of experience, with less experienced radiologists declining in sensitivity and lesion sensitivity while more experienced radiologists actually improve. With the increasing demands on radiologists to undertake high-volume reporting, we suggest that junior radiologists be made aware of possible sensitivity and lesion sensitivity deterioration over time so they can schedule breaks during continuous reading sessions that are appropriate to them, rather than try to emulate their more experienced colleagues.

ADVANCES IN KNOWLEDGE

Less-experienced radiologists demonstrated a reduction in mammographic diagnostic accuracy in later stages of the reporting sessions. This may suggest that extending the duration of reporting sessions to compensate for increasing workloads may not represent the optimal solution for less-experienced radiologists.

Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration

Medical image interpretation is central to detecting, diagnosing, and staging cancer and many other disorders. At a time when medical imaging is being transformed by digital technologies and artificial intelligence, understanding the basic perceptual and cognitive processes underlying medical image interpretation is vital for increasing diagnosticians’ accuracy and performance, improving patient outcomes, and reducing diagnostician burn-out. Medical image perception remains substantially understudied. In September of 2019, the National Cancer Institute convened a multidisciplinary panel of radiologists and pathologists together with researchers working in medical image perception and adjacent fields of cognition and perception for the “Cognition and Medical Image Perception Think Tank.” The Think Tank’s key objectives were: to identify critical unsolved problems related to visual perception in pathology and radiology from the perspective of diagnosticians; to discuss how these clinically relevant questions could be addressed through cognitive and perception research; to identify barriers and solutions for transdisciplinary collaborations; to define ways to elevate the profile of cognition and perception research within the medical image community; to determine the greatest needs to advance medical image perception; and to outline future goals and strategies to evaluate progress. The Think Tank emphasized diagnosticians’ perspectives as the crucial starting point for medical image perception research, with diagnosticians describing their interpretation process and identifying perceptual and cognitive problems that arise. This paper reports the deliberations of the Think Tank participants to address these objectives and highlight opportunities to expand research on medical image perception.

Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence

Background Missed fractures are a common cause of diagnostic discrepancy between initial radiographic interpretation and the final read by board-certified radiologists. Purpose To assess the effect of assistance by artificial intelligence (AI) on diagnostic performances of physicians for fractures on radiographs. Materials and Methods This retrospective diagnostic study used the multi-reader, multi-case methodology based on an external multicenter data set of 480 examinations with at least 60 examinations per body region (foot and ankle, knee and leg, hip and pelvis, hand and wrist, elbow and arm, shoulder and clavicle, rib cage, and thoracolumbar spine) between July 2020 and January 2021. Fracture prevalence was set at 50%. The ground truth was determined by two musculoskeletal radiologists, with discrepancies solved by a third. Twenty-four readers (radiologists, orthopedists, emergency physicians, physician assistants, rheumatologists, family physicians) were presented the whole validation data set (n = 480), with and without AI assistance, with a 1-month minimum washout period. The primary analysis had to demonstrate superiority of sensitivity per patient and the noninferiority of specificity per patient at -3% margin with AI aid. Stand-alone AI performance was also assessed using receiver operating characteristic curves. Results A total of 480 patients were included (mean age, 59 years ± 16 [standard deviation]; 327 women). The sensitivity per patient was 10.4% higher (95% CI: 6.9, 13.9; P < .001 for superiority) with AI aid (4331 of 5760 readings, 75.2%) than without AI (3732 of 5760 readings, 64.8%). The specificity per patient with AI aid (5504 of 5760 readings, 95.6%) was noninferior to that without AI aid (5217 of 5760 readings, 90.6%), with a difference of +5.0% (95% CI: +2.0, +8.0; P = .001 for noninferiority). AI shortened the average reading time by 6.3 seconds per examination (95% CI: -12.5, -0.1; P = .046). The sensitivity by patient gain was significant in all regions (+8.0% to +16.2%; P < .05) but shoulder and clavicle and spine (+4.2% and +2.6%; P = .12 and .52). Conclusion AI assistance improved the sensitivity and may even improve the specificity of fracture detection by radiologists and nonradiologists, without lengthening reading time. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Link and Pedoia in this issue.

Double Reading of Outsourced CT/MR Radiology Reports: Retrospective Analysis

OBJECTIVES

Our objective was to determine disagreement rates in radiological reports provided by using a double-reading protocol in a national teleradiology company.

METHODS

From January 2015 to July 2016, 134169 radiological exams from 36 French centers, benefited outsourced interpretations by certified radiologists, in both regular and after-hours activities. Of these, 2040 CT and MR-scans (1.5%) were subjected to a second opinion by other radiologists in the field of their anatomical specialty (cerebral, thoracic, abdominal-pelvic, and osteoarticular). A five-point agreement scale graded from 0 to 4 was assigned for each exam. Disagreements were considered as minor if no clinical consequence for patient (scores 1 and 2) and major if potential clinical consequence (score 3 and 4). Independent radiologists performed a retrospective analysis and a stratified statistical analysis.

RESULTS

Double reading was performed on CT-scans (n = 934/2040, 45.8%) and MR-scans (n = 1106/2040, 54.2%) performed in regular (80.1%) and after-hours activities (19.9%). Disagreement scores occurred in 437 exams (21.4%), including major disagreements in 59 (2.9%). Among these, 48/754 were assigned by the thoracic second reader (6.4%), 6/70 by the abdominal-pelvic second reader (8.6%), 3/901 by the osteoarticular second reader (0.3%), and 2/315 by the cerebral second reader (0.6%), with statistical significant difference. No additional disagreement rate was observed in regular and after-hours activities (P = 0.63).

CONCLUSIONS

Double-reading of outsourced CT and MRI interpretations yielded 21.4% disagreement rate, with potential clinical consequence for patient in 2,9% of the cases. These results are in accordance with those previously reported and suggests that quality assurance of outsourced interpretations is needed.

PROVIDER TECHNOLOGY, FATIGUE AND WELL-BEING: A SCOPING REVIEW (Preprint)

Background

Video and other technologies are reshaping the delivery of health care, yet barriers related to workflow and possible provider fatigue suggest that a thorough evaluation is needed for quality and process improvement.

Objective

This scoping review explored the relationship among technology, fatigue, and health care to improve the conditions for providers.

Methods

A 6-stage scoping review of literature (from 10 databases) published from 2000 to 2020 that focused on technology, health care, and fatigue was conducted. Technologies included synchronous video, telephone, informatics systems, asynchronous wearable sensors, and mobile health devices for health care in 4 concept areas related to provider experience: behavioral, cognitive, emotional, and physical impact; workplace at the individual, clinic, hospital, and system or organizational levels; well-being, burnout, and stress; and perceptions regarding technology. Qualitative content, discourse, and framework analyses were used to thematically analyze data for developing a spectrum of health to risk of fatigue to manifestations of burnout.

Results

Of the 4221 potential literature references, 202 (4.79%) were duplicates, and our review of the titles and abstracts of 4019 (95.21%) found that 3837 (90.9%) were irrelevant. A full-text review of 182 studies revealed that 12 (6.6%) studies met all the criteria related to technology, health care, and fatigue, and these studied the behavioral, emotional, cognitive, and physical impact of workflow at the individual, hospital, and system or organizational levels. Video and electronic health record use has been associated with physical eye fatigue; neck pain; stress; tiredness; and behavioral impacts related to additional effort owing to barriers, trouble with engagement, emotional wear and tear and exhaustion, cognitive inattention, effort, expecting problems, multitasking and workload, and emotional experiences (eg, anger, irritability, stress, and concern about well-being). An additional 14 studies that evaluated behavioral, emotional, and cognitive impacts without focusing on fatigue found high user ratings on data quality, accuracy, and processing but low satisfaction with clerical tasks, the effort required in work, and interruptions costing time, resulting in more errors, stress, and frustration. Our qualitative analysis suggests a spectrum from health to risk and provides an outline of organizational approaches to human factors and technology in health care. Business, occupational health, human factors, and well-being literature have not studied technology fatigue and burnout; however, their findings help contextualize technology-based fatigue to suggest guidelines. Few studies were found to contextually evaluate differences according to health professions and practice contexts.

Conclusions

Health care systems need to evaluate the impact of technology in accordance with the Quadruple Aim to support providers’ well-being and prevent workload burden, fatigue, and burnout. Implementation and effectiveness approaches and a multilevel approach with objective measures for clinical, human factors, training, professional development, and administrative workflow are suggested. This requires institutional strategies and competencies to integrate health care quality, technology and well-being outcomes.

VIEWDEX 3.0-RECENT DEVELOPMENT OF A SOFTWARE APPLICATION FACILITATING ASSESSMENT OF IMAGE QUALITY AND OBSERVER PERFORMANCE

ViewDEX (Viewer for Digital Evaluation of X-ray Images) is an image viewer compatible with Digital Imaging and Communications in Medicine (DICOM) that has been especially designed to facilitate image perception and observer performance studies within medical imaging. The software was first released in 2004 and since then a continuous development has been ongoing. One of the major drawbacks of previous versions of ViewDEX has been that they have lacked functionality enabling the possibility to evaluate multiple images and/or image stacks simultaneously. This functionality is especially requested by researchers working with modalities, where an image acquisition can result in multiple image stacks (e.g. axial, coronal and sagittal reformations in computed tomography). In ViewDEX 3.0 this functionality has been added and it is now possible to perform image evaluations of multiple images and/or image stacks simultaneously, by using multiple monitors and/or multiple image canvases in monitors. Additionally, some of the previously available functionality has been updated and improved. This paper describes the recent developments of ViewDEX 3.0.

Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network

OBJECTIVE

To develop a convolutional neural network (CNN) model for the detection, precise anatomical localization (right 1-12th and left 1-12th) and classification (fresh, healing and old fractures) of rib fractures automatically, and to compare the performance with the experienced radiologists.

MATERIALS AND METHODS

A total of 640 rib fracture patients with 340,501 annotations were retrospectively collected from three hospitals. They consisted of a classification training dataset (n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90) and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing and the result merging technique were employed to structure the CNN model. ROC curve, free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose the better option between model I (training classification and localization data together) and model II (adding an additional classification model to model I).

RESULTS

The detection and classification performance of rib fractures was better in model II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the right and left ribs, respectively. In the external dataset with different CT scanner and slice thickness, model II showed better diagnostic performance. Moreover, the CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists and consumed shorter diagnosis time.

CONCLUSIONS

Our CNN model was capable of detection, precise anatomical localization, and classification of rib fractures automatically.

Creating a Competency-Based Medical Education Curriculum for Canadian Diagnostic Radiology Residency (Queen’s Fundamental Innovations in Residency Education)—Part 1: Transition to Discipline and Foundation of Discipline Stages

Purpose:

The Royal College of Physicians and Surgeons of Canada (RCPSC) has mandated the transition of postgraduate medical training in Canada to a competency-based medical education (CBME) model divided into 4 stages of training. As part of the Queen’s University Fundamental Innovations in Residency Education proposal, Queen’s University in Canada is the first institution to transition all of its residency programs simultaneously to this model, including Diagnostic Radiology. The objective of this report is to describe the Queen’s Diagnostic Radiology Residency Program’s implementation of a CBME curriculum.

Methods:

At Queen’s University, the novel curriculum was developed using the RCPSC’s competency continuum and the CanMEDS framework to create radiology-specific entrustable professional activities (EPAs) and milestones. In addition, new committees and assessment strategies were established. As of July 2015, 3 cohorts of residents (n = 9) have been enrolled in this new curriculum.

Results:

EPAs, milestones, and methods of evaluation for the Transition to Discipline and Foundations of Discipline stages, as well as the opportunities and challenges associated with the implementation of a competency-based curriculum in a Diagnostic Radiology Residency Program, are described. Challenges include the increased frequency of resident assessments, establishing stage-specific learner expectations, and the creation of volumetric guidelines for case reporting and procedures.

Conclusions:

Development of a novel CBME curriculum requires significant resources and dedicated administrative time within an academic Radiology department. This article highlights challenges and provides guidance for this process.

Digital Eye Strain Among Radiologists: A Survey-based Cross-sectional Study

BACKGROUND

Computers have become a fundamental part of clinical radiology departments. Radiologists tend to spend long hours in front of computers, reading and analyzing medical images. This prolonged use of computers is associated with digital eye strain. Therefore, this study aimed to estimate the prevalence of digital eye strain among radiologists and determine its contributory factors.

METHODS

An online survey was sent to radiologists practicing in hospitals in the Eastern Province of Saudi Arabia. The survey addressed demographic information, workload and workstation environment, personal eye care, and evaluation of digital eye strain symptoms as well as the strategies employed to reduce these symptoms. Results were analyzed descriptively using Chi-square tests and logistic regression analyses.

RESULTS

The survey was completed by 198 participants (111 men and 87 women), including residents (40.9%), senior registrars (27.3%), and consultants (27.3%). Most participants (71.2%) were aged below 40 years. Most participants tend to spend 7-9 hours daily reviewing medical images. Overall, 50 participants (25.3%) take a break from work once daily only. A total of 53 participants (26.8%) reported undergoing an eye examination within the past year and 100 participants (50.5%) reported experiencing digital eye strain. Multivariate logistic regression analysis revealed that female sex (odds ratio [OR] = 3.9; 95% confidence interval [95% CI]: 1.6-10.0) and the practice of taking breaks once a day (OR = 15.1; 95% CI: 2.4-94.1) or twice a day (OR = 5.5; 95% CI: 1.1-28.4) only were associated with higher rates of digital eye strain symptoms.

CONCLUSION

Digital eye strain is a prevalent condition among radiologists regardless of their subspecialty. It is more commonly seen among radiology residents. Being a female and not taking frequent breaks were associated with higher rates of digital eye strain.

The impact of reader fatigue on the accuracy of capsule endoscopy interpretation

BACKGROUND AND AIMS

Capsule endoscopy (CE) interpretation requires the review of many thousands of images, with lesions often limited to just a few frames. In this study we aim to determine whether lesion detection declines according to the number of capsule videos read.

METHODS

32 participants, 16 of which were novices (NR) and 16 experienced (ER) capsule readers took part in this prospective evaluation study. Participants read six capsule cases with a variety of lesions, in a randomly assigned order during a single sitting. Psychomotor Vigilance Tests and Fatigue Scores were recorded prior to commencing and then after every two capsules read. Changes in lesion detection and measures of fatigue were assessed across the duration of the study.

RESULTS

Mean agreement with the predefined lesions was 48.3% (SD:16.1), and 21.3% (SD:15.1) for the experienced and novice readers respectively. Lesion detection declined amongst experienced reader after the first study (p = 0.01), but remained stable after subsequent capsules read, while NR accuracy was unaffected by capsule numbers read. Objective measures of fatigue did not correlate with reading accuracy.

CONCLUSION

This study demonstrates that reader accuracy declines after reading just one capsule study. Subjective and objective measures of fatigue were not sufficient to predict the onset of the effects of fatigue.

Could ante-mortem computed tomography be useful in forensic pathology of traumatic intracranial haemorrhage?

Background

Imaging techniques have proven valuable in forensic pathology practice, with computed tomography being preferred for forensic use. In the era of virtual autopsy and a low- to middle-income, resource-constrained country, a question arises as to whether ante-mortem computed tomography (ACT) could be cost-effective by reducing the number of invasive autopsies performed.

Objective

The objective of this study was to assess the usefulness of ACT in forensic pathology by examining discrepancy rates between ACT scans and autopsy findings in cases of deceased individuals with traumatic intracranial haemorrhages and assess factors associated with discrepancies.

Methods

Eighty-five cases of ACT and autopsy reports from 01 January 2014 to 31 December 2016 from the Polokwane Forensic Pathology Laboratory, South Africa, were analysed retrospectively. Using Cohen’s kappa statistics, measures of agreement and resultant discrepancy rates were determined. Also, the discrepancy patterns for each identified factor was also analysed.

Results

The discrepancy rate between ACT and autopsy detection of haemorrhage was 24.71% while diagnostic categorisation of haemorrhage was 55.3%. Classification discrepancy was most observed in subarachnoid haemorrhages and least observed in extradural haemorrhages. A markedly reduced level of consciousness, hospital stay beyond two weeks and three or fewer years of doctors’ experience contributed to classification discrepancies.

Conclusion

Ante-mortem computed tomography should be used only as an adjunct to autopsy findings. However, the low discrepancy rate seen for extradural haemorrhages implies that ACT may be useful in the forensic diagnosis of extradural haemorrhages.

Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Objective

To determine the anticipated contribution of recently published medical imaging literature, including artificial intelligence (AI), on the workload of diagnostic radiologists.

Methods

This study included a random sample of 440 medical imaging studies published in 2019. The direct contribution of each study to patient care and its effect on the workload of diagnostic radiologists (i.e., number of examinations performed per time unit) was assessed. Separate analyses were done for an academic tertiary care center and a non-academic general teaching hospital.

Results

In the academic tertiary care center setting, 65.0% (286/440) of studies could directly contribute to patient care, of which 48.3% (138/286) would increase workload, 46.2% (132/286) would not change workload, 4.5% (13/286) would decrease workload, and 1.0% (3/286) had an unclear effect on workload. In the non-academic general teaching hospital setting, 63.0% (277/240) of studies could directly contribute to patient care, of which 48.7% (135/277) would increase workload, 46.2% (128/277) would not change workload, 4.3% (12/277) would decrease workload, and 0.7% (2/277) had an unclear effect on workload. Studies with AI as primary research area were significantly associated with an increased workload (p < 0.001), with an odds ratio (OR) of 10.64 (95% confidence interval (CI) 3.25–34.80) in the academic tertiary care center setting and an OR of 10.45 (95% CI 3.19–34.21) in the non-academic general teaching hospital setting.

Conclusions

Recently published medical imaging studies often add value to radiological patient care. However, they likely increase the overall workload of diagnostic radiologists, and this particularly applies to AI studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01031-4.

Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images

Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).

Use of ambient lighting during colonoscopy and its effect on adenoma detection rate and eye fatigue: results of a pilot study

Background and study aims  Adenoma detection rate (ADR) appears to decrease as the number of consecutive hours performing procedures increases, and eye strain may be a contributing factor. Ambient light may improve symptoms of eye strain, but its effects have yet to be explored in the field of gastroenterology. We aim to determine if using ambient lighting during screening colonoscopy will maintain ADRs and improve eye strain symptoms compared with low lighting. Methods  At a single center, retrospective data were collected on colonoscopies performed under low lighting and compared to prospective data collected on colonoscopies with ambient lighting. Eye fatigue surveys were completed by gastroenterologists. Satisfaction surveys were completed by physicians and staff. Results  Of 498 low light and 611 ambient light cases, 172 and 220 adenomas were detected, respectively ( P  = 0.611). Under low lighting, the ADR decreased 5.6 % from first to last case of the day ( P  = 0.2658). With ambient lighting, the ADR increased by 2.80 % ( P  = 0.5445). The difference in the overall change in ADR between first and last cases with ambient light versus low light was statistically significant (8.40 % total unit change, P  = 0.01). The average eye strain scores were 8.12 with low light, and 5.63 with ambient light ( P  = 0.3341). Conclusions  Performing screening colonoscopies with ambient light may improve the differential change in ADR that occurs from the beginning to the end of the day. This improvement in ADR may be related to improvement in operator fatigue. The effect of ambient light on eye strain is unclear. Further investigation is warranted on the impact of ambient light on symptoms of eye strain and ADR.