UNASSIGNED: Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer.
UNASSIGNED: This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation.
UNASSIGNED: The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023.
UNASSIGNED: Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer.
UNASSIGNED: None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage.
UNASSIGNED: This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified.
UNASSIGNED: There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons.
UNASSIGNED: Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication.
UNASSIGNED: This study is registered as PROSPERO CRD42023384164.
UNASSIGNED: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.
Lung cancer is one of the most common types of cancer in the United Kingdom. Early diagnosis may improve survival, as lung cancer is often diagnosed late. Chest X-rays can be used to identify features of lung cancer. There can be delays in getting X-rays, and sometimes features of lung cancer are not seen on them. Artificial intelligence software may help by finding features of cancer on chest X-rays and highlighting them. A radiologist will look at the X-rays and information from the software. There is a lack of information about how lung cancer diagnosis could change if artificial intelligence software is used and what the costs may be to the National Health Service. This project looked at the use of artificial intelligence software in the detection of lung cancer in people referred from primary care. Software companies were invited to provide evidence. There were no studies that looked at this topic among people from primary care. We summarised the closest evidence we could find instead. All of this had flaws, so we could not tell if the results were accurate or helpful to this review. It was not clear if artificial intelligence helped to find cancers or improve people’s health. We made a theoretical model to discuss the best way to assess if artificial intelligence software might be cost-effective in detecting lung cancer and what evidence would be needed to do this in a fully working model. Costs and alternative pricing models provided by five companies were used to calculate the cost of adding artificial intelligence software to review chest X-rays in people referred from their general practitioner, for the first 5 years, based on one National Health Service trust. Future studies are needed to identify the impact of adjunct artificial intelligence on test accuracy, clinical decision-making and patient outcomes (e.g. mortality and morbidity).

The morphological characteristics of the foot arch and the plantar soft tissue thickness are pivotal in assessing foot health, which is associated with various foot and ankle pathologies. By applying deep learning image segmentation techniques to lateral weight-bearing X-ray images, this study investigates the correlation between foot arch morphology (FAM) and plantar soft tissue thickness (PSTT), examining influences of age and sex. Specifically, we use the DeepLab V3+ network model to accurately delineate the boundaries of the first metatarsal, talus, calcaneus, navicular bones, and overall foot, enabling rapid and automated measurements of FAM and PSTT. A retrospective dataset containing 1497 X-ray images is analyzed to explore associations between FAM, PSTT, and various demographic factors. Our findings contribute novel insights into foot morphology, offering robust tools for clinical assessments and interventions. The enhanced detection and diagnostic capabilities provided by precise data support facilitate population-based studies and the leveraging of big data in clinical settings.

Food crops around the world are commonly contaminated with Aspergillus flavus, which can produce the carcinogenic mycotoxin aflatoxin B1 (AFB1). The objective of this study is to test an X-ray irradiation sterilization method for studying AFB1 in contaminated maize samples in the laboratory. Maize that had been naturally contaminated with 300 ppb AFB1 by the growth of aflatoxigenic A. flavus was ground and then irradiated at 0.0, 1.0, 1.5, 2.0, 2.5, and 3.0 kGy. A. flavus was quantified by dilution plating on potato dextrose agar (PDA) and modified Rose Bengal media (MDRB) for viability and qPCR for gene presence. AFB1 was quantified by HPLC and ELISA. A. flavus viability, but not gene copies, significantly decreased with increasing doses of radiation (PDA: p < 0.001; MDRB: p < 0.001; qPCR: p = 0.026). AFB1 concentration did not significantly change with increasing doses of radiation (HPLC: p = 0.153; ELISA: p = 0.567). Our results imply that X-ray irradiation is an effective means of reducing viable A. flavus without affecting AFB1 concentrations. Reducing the hazard of fungal spores and halting AFB1 production at the targeted dose are important steps to safely and reproducibly move forward research on the global mycotoxin challenge.

This study aims to provide radiation reference levels in orthopaedic surgery. A total of 753 procedures were collected within 1 y. Categories containing several similar procedures were created based on four criteria: same anatomical area, same level of complexity, only single procedures, and at least 10 cases per category. Exposure was defined in terms of air kerma-area product, fluoroscopy time, and air kerma at the patient entrance reference point. For common procedures, median effective doses to patient were calculated using the Monte Carlo Software PCXMC. Most irradiating procedure in this study i.e. intramedullary nailing of the proximal femur was equivalent to an air kerma at the patient entrance reference point of 37.1 mGy, which is ~50 times lower than the threshold for acute deterministic effects of radiation. Optimization remains a must to reduce the dose while maintaining the image quality and reducing the likelihood of stochastic effects.

Visual perception of X-radiation is a well-documented, but poorly understood phenomenon. Scotopic rod cells and rhodopsin have been implicated in visual responses to X-rays, however, some evidence suggests that X-rays excite the retina via a different mechanism than visible light. While rhodopsin\'s role in X-ray perception is unclear, the possibility that it could function as an X-ray receptor has led to speculation that it could act as a transgenically expressed X-ray receptor. If so, it could be used to transduce transcranial X-ray signals and control the activity of genetically targeted populations of neurons in a less invasive version of optogenetics, X-genetics. Here we investigate whether human rhodopsin (hRho) is capable of transducing X-ray signals when expressed outside of the retinal environment. We use a live-cell cAMP GloSensor luminescence assay to measure cAMP decreases in hRho-expressing HEK293 cells in response to visible light and X-ray stimulation. We show that cAMP GloSensor luminescence decreases are not observed in hRho-expressing HEK293 cells in response to X-ray stimulation, despite the presence of robust responses to visible light. Additionally, irradiation had no significant effect on cAMP GloSensor responses to subsequent visible light stimulation. These results suggest that ectopically expressed rhodopsin does not function as an X-ray receptor and is not capable of transducing transcranial X-ray signals into neural activity for X-ray mediated, genetically targeted neuromodulation.

OBJECTIVE: Evidence-based radiographic guidelines are used to justify the need for radiographs and prevent their overuse. This study aimed to assess whether 4th-year chiropractic interns at the International Medical University plan to use x-ray imaging in their future private practice in line with the principles taught throughout their chiropractic program and the evidence-based imaging guidelines.
METHODS: A survey questionnaire was distributed to 74 final year chiropractic interns, with 62 completed responses. The questionnaire consisted of 8 case scenarios representing potential chiropractic patients. The interns were asked to decide whether to x-ray the patient or not, and which x-ray views to request if they chose to x-ray the patient.
RESULTS: Results were compared with the gold standard using percentage agreement. The findings revealed that the chiropractic students adhered to the gold standard answers for 6 out of 8 cases. However, they did not perform well in selecting the correct x-ray views for the 3 cases where radiography was indicated by the gold standard.
CONCLUSIONS: Results suggest that while the interns have a good understanding of when radiography is necessary, they may need additional training in selecting the appropriate x-ray views for each case.

OBJECTIVE: Calibrated pelvic X-ray images are needed in the preoperative planning of total hip arthroplasty (THA) to predict component sizes. Errors and mismatch in the size of one or more components are reported, which can lead to clinically relevant complications. Our aim is to investigate whether we can solve the fundamental problem of X-ray calibration and whether traditional X-ray still has a place in preoperative planning despite improved radiological alternatives.
METHODS: Based on geometric and radiographic principles, we estimate that the magnification factor is adapted to the X-ray device and depends strongly on the source-image distance of the device. We analyse the errors of the various calibration methods and investigate which narrow range can be expected to show that the center of rotation is sufficiently accurate. Based on the results of several CT-scans we defined an adapted magnification factor and validated the degree of measurement accuracy.
RESULTS: The true magnification of objects on X-ray images depends mainly on the device settings. Stem size prediction is possible to a limited extent, with an error margin of 4.3%. Components can be predicted with a safety margin of one size up and down as with CT or 3D images. The prerequisite is that the source-image distance is greater than or equal to 120 cm, the table-image distance is known, and the object-image distance is estimated according to the patient\'s BMI. We defined a device-adapted magnification factor that simplifies the templating routine and can be used to obtain the most reliable preoperative dimensional measurements that can be expected from X-ray images. We found the error margin of the magnification factor with the highest degrees of prediction and precision.
CONCLUSIONS: Preoperative planning is reliable and reproducible using X-ray images if calibration is performed with the device-adapted magnification factor suggested in this paper.

UNASSIGNED: The use of artificial intelligence (AI) for chest X-ray (CXR) analysis is becoming increasingly prevalent in medical environments. This study aimed to determine whether AI in CXR can unexpectedly detect lung nodule detection and influence patient diagnosis and management in non-respiratory outpatient clinics.
UNASSIGNED: In this retrospective study, patients over 18 years of age, who underwent CXR at Yongin Severance Hospital outpatient clinics between March 2021 and January 2023 and were identified to have lung nodules through AI software, were included. Commercially available AI-based lesion detection software (Lunit INSIGHT CXR) was used to detect lung nodules.
UNASSIGNED: Out Of 56,802 radiographic procedures, 40,191 were from non-respiratory departments, with AI detecting lung nodules in 1,754 cases (4.4%). Excluding 139 patients with known lung lesions, 1,615 patients were included in the final analysis. Out of these, 30.7% (495/1,615) underwent respiratory consultation and 31.7% underwent chest CT scans (512/1,615). As a result of the CT scans, 71.5% (366 cases) were found to have true nodules. Among these, the final diagnoses included 36 lung cancers (7.0%, 36/512), 141 lung nodules requiring follow-up (27.5%, 141/512), 114 active pulmonary infections (22.3%, 114/512), and 75 old inflammatory sequelae (14.6%, 75/512). The mean AI nodule score for lung cancer was significantly higher than that for other nodules (56.72 vs. 33.44, p < 0.001). Additionally, active pulmonary infection had a higher consolidation score, and old inflammatory sequelae had the highest fibrosis score, demonstrating differences in the AI analysis among the final diagnosis groups.
UNASSIGNED: This study indicates that AI-detected incidental nodule abnormalities on CXR in non-respiratory outpatient clinics result in a substantial number of clinically significant diagnoses, emphasizing AI\'s role in detecting lung nodules and need for further evaluation and specialist consultation for proper diagnosis and management.

BACKGROUND: There is compelling evidence that AXRs have limited clinical value in the acute setting. Despite this, they are frequently used in many EDs. This quality improvement project (QIP) aimed to reduce unnecessary AXR use in a single-centre ED.
METHODS: All consecutive AXRs conducted on patients aged 16 years and above in a District General Hospital ED in England between 2 August 2021 and 5 June 2022 were included. This period of time was divided into a pre-intervention and intervention period, during which iterative plan-do-study-act cycles were undertaken to implement a wide range of educational and system level interventions.
RESULTS: 501 AXRs were performed during the QIP. The average number of AXRs per fortnight fell from 27.5 during the preintervention period to 17.6 during the intervention period and met criteria for special cause variation. No special cause variation in CT usage was observed, with an average number of 70.7 and 74 CT abdomen-pelvis scans during the preintervention and intervention periods, respectively. 119 (23.8%) AXRs showed acute and clinically significant findings, and of this group 118/119 (99.2%) underwent further imaging. In contrast, 382 (76.2%) AXRs had no acute or clinically significant findings, and of this group 344/382 (90.1%) proceeded to further imaging.
CONCLUSIONS: In this single-centre QIP, coordinated multidisciplinary interventions were effective in reducing unnecessary AXR usage without resulting in excess CTs. The methods and interventions described are easily reproducible at minimal expense and may be of interest to other departments undertaking quality improvement work in this area.

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient\'s 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.