A 5 year old boy with acute ly mphoblastic leukaemia on chemotherapy presented with chest pain and vomiting for two days after an elective procedure under general anaesthesia. H is ches t x-ray was remarkabl e for a promin ent halo sign, an air gap surrounding the he art indicat ing a large pneumope ricardium. Alth ough the pneu mo pericardium could not be appre ciated on an echocardiogram, the child developed clinical sig ns of cardiac t amponade. Attem pts to evacu ate the pneumopericardium were unsuccessful leading to death.

BACKGROUND: Lumbar spine diagnostic imaging reports may cause patient and clinician concern when clinically unimportant findings are not explicitly described as benign. Our primary aim was to determine the frequency that common, benign findings are reported in lumbar spine plain X-ray, computed tomography (CT) and magnetic resonance imaging (MRI) reports as either normal for age or likely clinically unimportant.
METHODS: We obtained 600 random de-identified adult lumbar spine imaging reports (200 X-ray, 200 CT and 200 MRI) from a large radiology provider. Only reports requested for low back pain were included. From the report text, one author extracted each finding (e.g., \'broad-based posterior disc bulge\') and whether it was present or absent (e.g., no disc bulge) until data saturation was reached, pre-defined as a minimum of 50 reports and no new/similar findings in the last ten reports within each imaging modality. Two authors independently judged whether each finding was likely clinically unimportant or important. For each likely clinicially unimportant finding they also determined if it had been explicitly reported to be benign (expressed as normal, normal for age, benign, clinically unimportant or non-significant).
RESULTS: Data saturation was reached after coding 262 reports (80 X-ray, 82 CT, 100 MRI). Across all reports we extracted 3,598 findings. Nearly all reports included at least one clinically unimportant finding (76/80 (95%) X-ray, 80/82 (98%) CT, 99/100 (99%) MRI). Over half of the findings (n = 2,062, 57%; 272 X-Ray, 667 CT, 1123 MRI) were judged likely clinically unimportant. Most likely clinically unimportant findings (90%, n = 1,854) were reported to be present on imaging (rather than absent) and of those only 18% (n = 331) (89 (35%) X-ray, 93 (16%) CT and 149 (15%) MRI) were explicitly reported as benign.
CONCLUSIONS: Lumbar spine imaging reports frequently include findings unlikely to be clinically important without explicitly qualifying that they are benign.

OBJECTIVE: During any radiological procedure, it is important to know the dose to be-administered to the patient and this can be done by estimating the output of the X-ray tube either with a dosimeter or with a mathematical equation or Monte Carlo simulations. The aim of this work is to develop a new mathematical model equation (NMME) for estimating the output of high-frequency X-ray tubes.
METHODS: To achieve this, data collected from ten machines in many regions of Cameroon were used (for nine machines) to build an initial model that does not take into account the anode angle and the tenth machine was used to test the model. Using the SpekCalc software, some simulations were carried out to evaluate the influence of the anode angle. This allowed the NMME to be proposed.
RESULTS: The deviations frequencies between 0.65% and 19.61% were obtained by comparing the output values obtained using initial model with the measured values. The statistical hypothesis test showed that the estimated values using initial model and NMME are in agreement with those measured unlike the Kothan and Tungjai model. For the tenth machine, the percentage difference between estimated and measured values is less than 8 %.
CONCLUSIONS: These results show that the proposed model performed better than the previous models. In the absence of a dosimeter, the NMME could be used to estimate the output of high frequency X-ray machines and therefore the radiation doses received by patients during diagnostic X-ray examinations.

Despite its undeniable advantages, the operation of a CT scanner also carries risks to human health. The CT scanner is a source of ionizing radiation, which also affects people in its surroundings. The aim of this paper is to quantify the radiation exposure of workers at a 3D CT wood scanning workplace and to determine a monitoring program based on measurements of ionizing radiation levels during the operation of a CT log scanner. The workplace is located in the Biotechnology Park of the National Forestry Centre. The ionizing radiation source is located in a protective cabin as a MICROTEC 3D CT machine with an X-ray lamp as X-ray source. The CT scanner is part of the 3D CT scanning line and its function is continuous quality scanning or detection of internal defects of the examined wood. The measurement of leakage radiation during scanning is performed with a metrologically verified meter. The measured quantity is the ambient dose equivalent rate H˙*10. The results of the measurements at the selected measurement sites have shown that, after installation of additional safety barriers, the CT scanner for the logs complies with the most strict criteria in terms of radiation protection. Workers present at the workplace during the operation of the CT scanner are not exposed to radiation higher than the background radiation level.

Recent advances in artificial intelligence (AI) have sparked interest in developing explainable AI (XAI) methods for clinical decision support systems, especially in translational research. Although using XAI methods may enhance trust in black-box models, evaluating their effectiveness has been challenging, primarily due to the absence of human (expert) intervention, additional annotations, and automated strategies. In order to conduct a thorough assessment, we propose a patch perturbation-based approach to automatically evaluate the quality of explanations in medical imaging analysis. To eliminate the need for human efforts in conventional evaluation methods, our approach executes poisoning attacks during model retraining by generating both static and dynamic triggers. We then propose a comprehensive set of evaluation metrics during the model inference stage to facilitate the evaluation from multiple perspectives, covering a wide range of correctness, completeness, consistency, and complexity. In addition, we include an extensive case study to showcase the proposed evaluation strategy by applying widely-used XAI methods on COVID-19 X-ray imaging classification tasks, as well as a thorough review of existing XAI methods in medical imaging analysis with evaluation availability. The proposed patch perturbation-based workflow offers model developers an automated and generalizable evaluation strategy to identify potential pitfalls and optimize their proposed explainable solutions, while also aiding end-users in comparing and selecting appropriate XAI methods that meet specific clinical needs in real-world clinical research and practice.

Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones. However, the challenges associated with such systems have not been studied to the best of our knowledge. In this paper, we show that the predictions of AI-driven models on CXR images transmitted through Smart Phones via applications, such as WhatsApp, suffer both in terms of Predictability and Explainability, two key aspects of any automated Medical Diagnosis system. We find that several existing Deep learning based models exhibit prediction instability-disagreement between the prediction outcome of the original image and the transmitted image. Concomitantly we find that the explainability of the models deteriorate substantially, prediction on the transmitted CXR is often driven by features present outside the lung region, clearly a manifestation of Spurious Correlations. Our study reveals that there is significant compression of high-resolution CXR images, sometimes as high as 95%, and this could be the reason behind these two problems. Apart from demonstrating these problems, our main contribution is to show that Multi-Task learning (MTL) can serve as an effective bulwark against the aforementioned problems. We show that MTL models exhibit substantially more robustness, 40% over existing baselines. Explainability of such models, when measured by a saliency score dependent on out-of-lung features, also show a 35% improvement. The study is conducted on WaCXR dataset, a curated dataset of 6562 image pairs corresponding to original uncompressed and WhatsApp compressed CXR images. Keeping in mind that there are no previous datasets to study such problems, we open-source this data along with all implementations.

Continuous release of image databases with fully or partially identical inner categories dramatically deteriorates the production of autonomous Computer-Aided Diagnostics (CAD) systems for true comprehensive medical diagnostics. The first challenge is the frequent massive bulk release of medical image databases, which often suffer from two common drawbacks: image duplication and corruption. The many subsequent releases of the same data with the same classes or categories come with no clear evidence of success in the concatenation of those identical classes among image databases. This issue stands as a stumbling block in the path of hypothesis-based experiments for the production of a single learning model that can successfully classify all of them correctly. Removing redundant data, enhancing performance, and optimizing energy resources are among the most challenging aspects. In this article, we propose a global data aggregation scale model that incorporates six image databases selected from specific global resources. The proposed valid learner is based on training all the unique patterns within any given data release, thereby creating a unique dataset hypothetically. The Hash MD5 algorithm (MD5) generates a unique hash value for each image, making it suitable for duplication removal. The T-Distributed Stochastic Neighbor Embedding (t-SNE), with a tunable perplexity parameter, can represent data dimensions. Both the Hash MD5 and t-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and Coronavirus Disease of 2019 (COVID-19). We evaluated the performance of all proposed data and the new automated version using the Inception V3 pre-trained model with various evaluation metrics. The performance outcome of the proposed scale model showed more respectable results than traditional data aggregation, achieving a high accuracy of 98.48%, along with high precision, recall, and F1-score. The results have been proved through a statistical t-test, yielding t-values and p-values. It\'s important to emphasize that all t-values are undeniably significant, and the p-values provide irrefutable evidence against the null hypothesis. Furthermore, it\'s noteworthy that the Final dataset outperformed all other datasets across all metric values when diagnosing various lung infections with the same factors.

To report preliminary findings of hypofractionated superficial radiotherapy for treatment of cutaneous mast cell tumors (MCTs) and report the acute and late toxicity associated with its use.
3 dogs and 1 cat.
In this retrospective study, medical records from January 2021 through July 2022 were searched for animals that received superficial radiation therapy for MCTs of the head.
4 patients with 5 MCTs were included. Three of the masses were periocular and required protection of the globe with a tungsten eye shield. One patient did not complete the intended protocol due to diffuse metastatic spread noted after the second fraction. Of the 3 patients that completed their protocol, 100% had a complete response. Two canine patients were treated adjunctively with toceranib. Two of the 4 patients experienced grade 1 acute veterinary radiation therapy oncology group (VRTOG) toxicity, and the 3 patients that completed their protocol experienced grade 1 late VRTOG toxicity. No radiation effects were documented to the cornea or lens in any patient.
Superficial radiation therapy was effective in our limited study population, and patients experienced minimal side effects for treatment of cutaneous MCTs.

暂无摘要。

UNASSIGNED: Late diagnosed Developmental Dysplasia of the Hip (DDH) is the detection of DDH after 3 months of age and is associated with significantly poorer outcomes than when diagnosed and managed early. Late diagnosed DDH has lower rates of success with bracing, higher rates of surgery and higher rates of complications, including avascular necrosis of the femoral head and early osteoarthritis of the hip. We describe two cases of late-diagnosed DDH which demonstrated changes in femoroacetabular joint morphology on radiographic interpretation after a 6-month trial period of manual therapy.
UNASSIGNED: Two cases (13 and 30 months of age) with late-diagnosed DDH presented to a private chiropractic clinic for conservative, non-bracing management. One case had unilateral DDH and the other bilateral DDH. A trial of manual therapy was utilized over a 6-month period. Both cases demonstrated changes to femoroacetabular morphology as well as improvements in gross motor activity and lower extremity muscle tone.
UNASSIGNED: Manual therapy, as an adjunct or alternative to static bracing, may be of benefit in individuals with late-diagnosed DDH not responding to bracing, and prior to more invasive interventions. Additional cases of manual therapy-based management of this condition are required to inform the design of future trials to investigate this hypothesis.