BACKGROUND: Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists; however, it can increase the infection risk for healthcare professionals amidst the coronavirus pandemic. In recent years, with the advancement of artificial intelligence (AI), its application in medical imaging has also thrived. This research is to identify the optimal convolutional neural network (CNN) algorithm for accurate diagnosis of tonsillitis and early precision treatment.
METHODS: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our CNN, with the algorithm including UNet, PSPNet, and FPN. A total of 485 pharyngoscopic images from 485 participants were included, comprising healthy individuals (133 cases), patients with the common cold (295 cases), and patients with tonsillitis (57 cases). Both color and texture features from 485 images are extracted for analysis.
RESULTS: UNet outperformed PSPNet and FPN in accurately segmenting oropharyngeal anatomy automatically, with average Dice coefficient of 97.74% and a pixel accuracy of 98.12%, making it suitable for enhancing the diagnosis of tonsillitis. The normal tonsils generally have more uniform and smooth textures and have pinkish color, similar to the surrounding mucosal tissues, while tonsillitis, particularly the antibiotic-required type, shows white or yellowish pus-filled spots or patches, and shows more granular or lumpy texture in contrast, indicating inflammation and changes in tissue structure. After training with 485 cases, our algorithm with UNet achieved accuracy rates of 93.75%, 97.1%, and 91.67% in differentiating the three tonsil groups, demonstrating excellent results.
CONCLUSIONS: Our research highlights the potential of using UNet for fully automated semantic segmentation of oropharyngeal structures, which aids in subsequent feature extraction, machine learning, and enables accurate AI diagnosis of tonsillitis. This innovation shows promise for enhancing both the accuracy and speed of tonsillitis assessments.

BACKGROUND: The College of Radiographers\' vision was that diagnostic radiographers in the UK would be writing preliminary clinical evaluations (PCE) on images. Their 2013 policy supporting the use of PCE has not been updated in a decade and it might be suggested PCE practices in the UK have not really moved on, though elsewhere it appears to have gained traction. The aim of this scoping review was to establish the current global status of the use of PCE.
METHODS: The Arksey and O\'Malley scoping review framework and PRISMA-ScR guidelines were used to develop a protocol to identify studies between January 2013 to January 2024 using six databases. Collated literature was analysed using content analysis to identify themes.
RESULTS: 52 relevant studies were identified for inclusion. Studies focused predominantly on evaluating accuracy, education, perceptions, and new initiatives. Themes identified a developing role in the use of PCE internationally, perhaps more than in the UK, and in a range of modalities and clinical settings though projectional radiography remains the mainstay. Barriers and drivers to the use of PCE were identified in addition to some quality mechanisms used to support PCE implementation, though impact of implementation was not well explored.
CONCLUSIONS: Considering PCE has been an aspiration for more than a decade, it remains relatively infrequently researched. There is growing scope internationally, particularly in Australia, yet there is no real evaluation of the impact and role that PCE may have.
CONCLUSIONS: Until further research into the potential impact of PCE and barriers to its implementation, it is likely practices may not evolve with the risk AI technologies may supersede necessity for the practice.

OBJECTIVE: To investigate the associations between the hour of the day and Prostate Imaging-Reporting and Data System (PI-RADS) scores assigned by radiologists in prostate MRI reports.
METHODS: Retrospective single-center collection of prostate MRI reports over an 8-year period. Mean PI-RADS scores assigned between 0800 and 1800 h were examined with a regression model.
RESULTS: A total of 35\'004 prostate MRI interpretations by 26 radiologists were included. A significant association between the hour of day and mean PI-RADS score was identified (β2 = 0.005, p < 0.001), with malignant scores more frequently assigned later in the day.
CONCLUSIONS: These findings suggest chronobiological factors may contribute to variability in radiological assessments. Though the magnitude of the effect is small, this may potentially add variability and impact diagnostic accuracy.

BACKGROUND: Medical imaging\'s critical role in diagnosis requires prompt and precise image interpretation. Numerous radiology departments, especially in low-resourced settings, encounter challenges such as a shortage of radiologists in their operational setup. This study explored the perceptions of radiographers and radiologists from low-resourced departments in a single country regarding operational challenges and potential solutions in image interpretation.
METHODS: A qualitative approach was utilised, involving heads of departments, senior radiographers, and radiologists with a minimum of 5 years of experience, from three major state referral hospitals. Face-to-face, semi-structured interviews were conducted in November 2022, using an interview guide that included questions on the challenges encountered during image interpretation and the proposed solutions. Data analysis was conducted using Atlas.ti version 9.0, following the four-step content analysis method. All participants willingly provided consent to participate in the study.
RESULTS: Ten participants, comprising two radiologists and eight radiographers participated in the study. The research identified three main themes: image interpretation pathways, image interpretation operational challenges and proposed solutions for image interpretation. In addition, a total of 10 subthemes were generated from the three main themes.
CONCLUSIONS: The study revealed critical challenges and the need to explore the formal inclusion of radiographers in image interpretation, as a way to improve efficiency. However, a comprehensive assessment of the image interpretation system, encompassing radiographers\' knowledge and competence, is recommended for context-specific, empirical-based modifications to enhance service provision.

UNASSIGNED: Indiscriminate coronary computed tomography angiography (CCTA) referrals for suspected coronary artery disease could result in a higher rate of equivocal and non-diagnostic studies, leading to inappropriate downstream resource utilization or delayed time to diagnosis. We sought to develop a simple clinical tool for predicting the likelihood of a non-diagnostic CCTA to help identify patients who might be better served with a different test.
UNASSIGNED: We developed a clinical scoring system from a cohort of 21 492 consecutive patients who underwent CCTA between February 2006 and May 2021. Coronary computed tomography angiography study results were categorized as normal, abnormal, or non-diagnostic. Multivariable logistic regression analysis was conducted to produce a model that predicted the likelihood of a non-diagnostic test. Machine learning (ML) models were utilized to validate the predictor selection and prediction performance. Both logistic regression and ML models achieved fair discriminate ability with an area under the curve of 0.630 [95% confidence interval (CI) 0.618-0.641] and 0.634 (95% CI 0.612-0.656), respectively. The presence of a cardiac implant and weight >100 kg were among the most influential predictors of a non-diagnostic study.
UNASSIGNED: We developed a model that could be implemented at the \'point-of-scheduling\' to identify patients who would be best served by another non-invasive diagnostic test.

OBJECTIVE: To determine the optimal window setting for virtual monoenergetic images (VMI) reconstructed from dual-layer spectral coronary computed tomography angiography (DE-CCTA) datasets.
METHODS: 50 patients (30 males; mean age 61.1 ± 12.4 years who underwent DE-CCTA from May 2021 to June 2022 for suspected coronary artery disease, were retrospectively included. Image quality assessment was performed on conventional images and VMI reconstructions at 70 and 40 keV. Objective image quality was assessed using contrast-to-noise ratio (CNR). Two independent observers manually identified the best window settings (B-W/L) for VMI 70 and VMI 40 visualization. B-W/L were then normalized with aortic attenuation using linear regression analysis to obtain the optimized W/L (O-W/L) settings. Additionally, subjective image quality was evaluated using a 5-point Likert scale, and vessel diameters were measured to examine any potential impact of different W/L settings.
RESULTS: VMI 40 demonstrated higher CNR values compared to conventional and VMI 70. B-W/L settings identified were 1180/280 HU for VMI 70 and 3290/900 HU for VMI 40. Subsequent linear regression analysis yielded O-W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40. VMI 40 O-W/L received the highest scores for each parameter compared to conventional (all p < 0.0027). Using O-W/L settings for VMI 70 and VMI 40 did not result in significant differences in vessel measurements compared to conventional images.
CONCLUSIONS: Optimization of VMI requires adjustments in W/L settings. Our results recommend W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40.

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BACKGROUND: Radiographer commenting is a written account of suspected abnormalities identified on medical imaging examinations by the radiographer at the time of image acquisition. Radiographer comments were originally implemented to support emergency clinicians; however, they may also have the potential to support radiologists in reducing missed findings. Therefore, the aim of this study was to investigate if a newly implemented radiographer comment system could reduce the number of errors made in radiology reports for general X-rays. Incidental findings from multisite collaborative research led to the hypothesis that in some cases radiographer comments could accurately detect abnormal X-ray appearances that were not otherwise documented in the radiologist report, thereby enabling results to be revised and errors collaboratively reduced [1].
METHODS: This study was conducted at an 800-bed hospital, where 92% of general radiographers self-selected to participate. Radiographer comments were provided to referring physicians through the electronic medical record and could be made for any emergency or inpatient general X-ray examinations. All comments made over a 12-month period were audited against the corresponding radiologist report. Radiologists were blinded to radiographer comments at the time of reporting. Where discrepancies between the radiographer comment and radiologist report arose, additional radiologist review or subsequent imaging reports were used to determine the accurate interpretation. The number of discrepant radiographer comments that were deemed true positive (TP) and provided new and correct diagnostic information compared to the radiologist report were identified. These were converted to a percentage of total radiographer comments that were therefore able to positively influence radiologist report accuracy. The number of discrepant cases where radiographer comments were deemed false positive (FP) was also measured and converted to a percentage of the total comments. Confidence intervals for both TP and FP binomial proportions were calculated using the Wilson Score Interval.
RESULTS: Over 12 months, 282 radiographer comments were made to alert clinically significant radiographic appearances on general X-ray. Of these, 32 radiographer comments were discrepant with the report. Of these 32 comments, 24 were deemed TP meaning they correctly identified a pathological imaging appearance that was not otherwise documented in the radiology report. Therefore, 8.5% of all radiographer comments added value by correctly identifying a pathology that was not otherwise documented, 95% CI (5.8% - 12.4%). This enabled results to be promptly amended and reporting errors collaboratively reduced. Conversely, eight (2.8%) radiographer comments were discrepant with the report but deemed FP and did not add value to the investigation, 95% CI (1.4% - 5.5%). The remaining 250 non-discrepant comments did not contribute to error reduction but provided real-time abnormality detection that benefitted managing teams.
CONCLUSIONS: These findings are consistent with previous literature proposing radiographer comments may provide a safety net for radiologists due to factors such as direct patient contact, ability to expand on clinical history, and difference in accumulated expertise. This study demonstrates that radiographer comments may be effectively used as a multidisciplinary error-reduction tool to assist radiologists in their important role and improve clinical outcomes.

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

BACKGROUND: Policymakers wish to extend access to medical records, including medical imaging. Appreciating how patients might review radiographs could be key to establishing future training needs for healthcare professionals and how image sharing could be integrated into practice.
METHODS: A pilot study in the UK using a survey was distributed to adult participants via the online research platform Prolific. All subjects were without prior professional healthcare experience. Participants reviewed ten radiographs (single projection only) and were asked a two-stage question. Firstly, if the radiograph was \'normal\' or \'abnormal\' and secondly, if they had answered \'abnormal\', to identify the abnormality from a pre-determined list featuring generic terms for pathologies.
RESULTS: Fifty participants completed the survey. A mean of 65.8 % of participants were able to correctly identify if radiographs were normal or abnormal. Results in relation to the identification of a pathology were not as positive, but still notable with a mean of 46.4 % correctly identifying abnormalities. Qualitative data demonstrated that members of the public are enthralled with reviewing radiographs and intrigued to understand their performance in identifying abnormalities.
CONCLUSIONS: In the pilot, members of the public could identify if a radiograph is normal or abnormal to a reasonable standard. Further detailed interpretation of images requires supportive intervention. This pilot study suggests that patients can participate in image sharing as part of their care. Image sharing may be beneficial to the therapeutic relationship, aiding patient understanding and enhancing consultations between healthcare professional and patient. Further research is indicated.