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Objectives: The purpose of this study is to evaluate the performance of our deep learning algorithm in calculating cardiothoracic ratio (CTR) and thus in the assessment of cardiomegaly or pericardial effusion occurrences on chest radiography (CXR). Methods: From a database of 8000 CXRs, 13 folders with a comparable number of images were created. Then, 1020 images were chosen randomly, in proportion to the number of images in each folder. Afterward, CTR was calculated using RadiAnt Digital Imaging and Communications in Medicine (DICOM) Viewer software (2023.1). Next, heart and lung anatomical areas were marked in 3D Slicer. From these data, we trained an AI model which segmented heart and lung anatomy and determined the CTR value. Results: Our model achieved an Intersection over Union metric of 88.28% for the augmented training subset and 83.06% for the validation subset. F1-score for subsets were accordingly 90.22% and 90.67%. In the comparative analysis of artificial intelligence (AI) vs. humans, significantly lower transverse thoracic diameter (TTD) (p < 0.001), transverse cardiac diameter (TCD) (p < 0.001), and CTR (p < 0.001) values obtained using the neural network were observed. Conclusions: Results confirm that there is a significant correlation between the measurements made by human observers and the neural network. After validation in clinical conditions, our method may be used as a screening test or advisory tool when a specialist is not available, especially on Intensive Care Units (ICUs) or Emergency Departments (ERs) where time plays a key role.
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Background: A reliable assessment of liver volume, necessary before transplantation, remains a challenge. Our work aimed to assess the differences in the evaluation and measurements of the liver between independent observers and compare different formulas calculating its volume in relation to volumetric segmentation. Methods: Eight researchers measured standard liver dimensions based on 105 abdominal computed tomography (CT) scans. Based on the results obtained, the volume of the liver was calculated using twelve different methods. An independent observer performed a volumetric segmentation of the livers based on the same CT examinations. Results: Significant differences were found between the formulas and in relation to volumetric segmentation, with the closest results obtained for the Heinemann et al. method. The measurements of individual observers differed significantly from one another. The observers also rated different numbers of livers as enlarged. Conclusions: Due to significant differences, despite its time-consuming nature, the use of volumetric liver segmentation in the daily assessment of liver volume seems to be the most accurate method.
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Purpose: The aim of our study is to evaluate the impact of ultrasound probe mechanical pressure on the stiffness of the gastrocnemius muscle in a healthy paediatric population. As far as we know, there has been no previous qualitative in vivo study on the impact of probe pressure on muscle shear-wave elastography results with objective evaluation of compression in the paediatric population. Material and methods: In this cohort study, a group of 22 children (mean age 8.99 years, SD 2.74, 11 males) underwent elastography of the gastrocnemius muscle of the dominant leg. A custom-made, 3-dimensional printed probe cover was used to measure the mechanical pressure of the probe on tissues. Results: The obtained results were related to the age, sex, BMI, and calf circumference of the subjects. We observed a significant difference in the stiffness parameter at a pressure of 1 N, with a further increase if force was increased (p < 0.001). A significant, very weak positive correlation of age and stiffness was observed (p < 0.001, r2 = 0.022). There was no significant correlation of stiffness, BMI, and calf circumference. Conclusions: The use of compression during muscle elastography in children causes a significant bias in results, regardless of age, sex, BMI, or calf size.
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As the number of smartphones increases, so does the number of medical apps. Medical mobile applications are widely used in many medical fields by both patients and doctors. However, there are still few approved mobile applications that can be used in the diagnostic-therapeutic process and radiological apps are affected as well. We conducted our research by classifying radiological applications from the Google Play® store into appropriate categories, according to our own qualification system developed by researchers for the purposes of this study. In addition, we also evaluated apps from the App Store®. The radiology application rating system we created has not been previously used in other articles. Out of 228 applications from the Google Play store, only 6 of them were classified as "A" category with the highest standard. Apps from the App Store (157) were not categorized due to the lack of download counts, which was necessary in our app-rating system. The vast majority of applications are for educational purposes and are not used in clinical practice. This is due to the need of obtaining special permits and certificates from relevant institutions in order to use them in medical practice. We recommend applications from the Google Play store that have been classified in the "A" category, evaluating them as the most valuable. App Store apps data is described and presented in the form of diagrams and tables.
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Aplicaciones Móviles , Radiología , Humanos , Polonia , Teléfono InteligenteRESUMEN
In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve-AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units).
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Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.
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BACKGROUND: With the increased interest in inter-recti distance measurement using ultrasound imaging in physiotherapy, there is a question of measurement reliability, and the importance of the examiner's experience. PURPOSE: The study aimed to investigate the reliability of inter-recti distance measurement in a DICOM viewer software by an experienced radiologist. For the measurement, the radiologist used linea alba images captured by two physiotherapists who were novice examiners. METHODS: Ultrasound images were acquired by two novice examiners on repeated occasions 7 days apart (sessions A and B) in 28 nulliparous women at supraumbilical, umbilical, and infraumbilical locations along linea alba. RESULTS: Excellent intra-examiner reliability of inter-recti distance measurements was shown at the supraumbilical and umbilical levels (ICC2,k = 0.941-0.983) with minimal detectable change (MDC95) ranging from 1.31 mm to 2.29 mm. Infraumbilical measurements had good to excellent reliability (ICC2,k = 0.894-0.972) with MDC95 ranging from 0.33 mm to 0.72 mm. Session A inter-examiner reliability was excellent for the mean measurements of two, three, four, and five images taken at each location (ICC2,k = 0.913-0.954) with MDC95 ranging from 0.47 mm to 2.96 mm. Session B inter-examiner reliability was excellent for the mean measurements of two, three, four, and five images taken at the supraumbilical and umbilical (ICC2,k = 0.94-0.98), MDC95 ranging from 1.38 mm to 2.58 mm and good (ICC2,k ≥ 0.81) with MDC95 ranging from 0.72 mm to 0.80 mm at the infraumbilical locations. CONCLUSION: Novice examiners were able to capture good-quality ultrasound images of the linea alba that allowed for good to excellent intra- and inter-examiner reliability.
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BACKGROUND: The COVID-19 pandemic, which affected the entire global population, had an impact on our health and quality of life. Many people had complications, were hospitalised or even died due to SARS-CoV-2 infection. The health systems of many countries had to radically change their way of functioning and scientists around the world worked intensively to develop a vaccine for the SARS-CoV-2 virus. AIM: The aim of this work is to assess the quality of life of patients who were hospitalised for COVID-19, using the SF-36 questionnaire. METHODS: Between May and August 2022, we conducted a telephone assessment of quality of life in patients who were hospitalised for COVID-19 at the Temporary Hospital in Pyrzowice (Silesia, Poland), between November 2021 and January 2022. RESULTS: Quality of life was significantly lower in women (p = 0.040), those with DM2 (p = 0.013), CKD (p = 0.041) and the vaccinated (p = 0.015). CONCLUSIONS: People with chronic kidney disease, diabetes mellitus and women had a lower quality of life after COVID-19 disease. However, people who were vaccinated for SARS-CoV-2 had a lower quality of life than non-vaccinated people did. This is possibly due to the higher mean age, and probably the higher disease burden, in the vaccinated group.
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Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.
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The point shear wave elastography and supersonic shear imaging methods were compared regarding incorrect measurements during the liver examinations. A report-based, single-center, retrospective analysis of 425 liver elastography examinations was performed. A lower success ratio was observed for the point shear wave elastography method, as well as the older and obese patients pre-dominated in non-diagnostic studies. In our center experience, it is easier to obtain diagnostic data using the supersonic shear imaging method. However, further investigation of the subject is needed.
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Diagnóstico por Imagen de Elasticidad , Humanos , Diagnóstico por Imagen de Elasticidad/métodos , Estudios Transversales , Estudios Retrospectivos , Reproducibilidad de los Resultados , Hígado/diagnóstico por imagen , Hígado/patología , Cirrosis Hepática/diagnósticoRESUMEN
BACKGROUND: Inter-recti distance (IRD) measurement using musculoskeletal USI has been used in physiotherapy research, in particular, to investigate pregnancy-related diastasis recti abdominis (DRA) and to seek its effective treatment methods. Severe and untreated diastasis may result in the formation of umbilical or epigastric hernias. OBJECTIVE: This study aimed to systematically map physiotherapy-related research articles that included descriptions of IRD measurement procedures using USI to present their similarities and differences, and formulate recommendations on the procedure. DESIGN: A scoping review was conducted according to PRISMA-ScR guidelines, including 49 of 511 publications from three major databases. Publications were selected and screened by two independent reviewers whose decisions were consulted with a third reviewer. The main synthesized data items were: the examinees' body position, breathing phase, measurement sites, and DRA screening methods. The final conclusions and recommendations were the result of a consensus between seven reviewers from four research centers. RESULTS: Studies used 1-5 measurement sites that were differently determined. IRD was measured at the umbilicus (n = 3), at its superior (n = 16) and/or inferior border (n = 9), and at different levels: between 2 and 12 cm above the umbilicus, or a third of the distance and halfway between the umbilicus and xiphoid (n = 37); between 2 and 4.5 cm below the umbilicus or halfway between the umbilicus and pubis (n = 27). Different approaches were used to screen subjects for DRA. CONCLUSIONS: The discrepancies between the measurement procedures prevent between-study comparisons. The DRA screening method should be standardized. IRD measurement protocol standardization has been proposed. CRITICAL RELEVANCE STATEMENT: This scoping review indicates that the inter-recti distance measurement procedures using ultrasound imaging differ between studies, preventing between-study comparisons. Based on the results synthesis, the measurement protocol standardization has been proposed. KEY POINTS: The inter-recti distance measurement procedures using USI differ between studies. Proposed standardization concerns body position, breathing phase, measurements number per location. Determination of measurement locations considering individual linea alba length is suggested. Recommended locations: umbilical top, ½ of umbilical top-xiphoid, » of umbilical top-xiphoid/pubis distances. Diastasis recti abdominis diagnostic criteria are needed for proposed measurement locations.
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BACKGROUND: On March 11, 2020, coronavirus disease (COVID-19) was declared a global threat by the World Health Organization (WHO). It quickly became apparent that reducing inpatient mortality rates and early phase prediction of possible deterioration or severe disease course relied on finding more specific biomarkers. OBJECTIVES: This retrospective study assessed initial clinical, laboratory and radiological features of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients and explored their impact on mortality and the course of the disease. Such efforts aimed to facilitate the identification of high-risk patients and to improve the formulation of treatment plans for these individuals. MATERIAL AND METHODS: The cohort comprised 111 consecutive adult inpatients diagnosed with COVID-19 and hospitalized in the Internal Medicine Ward of the University Clinical Center of prof. K. Gibinski of the Medical University of Silesia in Katowice, Poland, a COVID-19 Treatment Unit, between November 16, 2020 and February 15, 2021. All available clinical, laboratory and radiological findings were extracted from electronic records and assessed as possible risk factors for poor prognosis. RESULTS: Clinicasl and radiological features with higher frequency in COVID-19 non-survivors included older age, history of smoking, concomitant cardiovascular diseases, low oxygen saturation (SpO2), and high infection risk assessed on admission as well as high opacity score, percentage of opacity and percentage of high opacity in computed tomography. Non-survivors had decreased serum lymphocytes, monocytes, calcium, magnesium, and hemoglobin oxygen saturation. They also had increased red cell distribution width (RDW), C-reactive protein (CRP), procalcitonin, alkaline phosphatase (ALP), creatinine, blood urea nitrogen (BUN), D-dimer, troponin, and N-terminal prohormone of brain natriuretic peptide (NT-proBNP) levels, as well as a base deficit. CONCLUSIONS: This retrospective study identified several markers associated with a fatal course of COVID-19. The early assessment of SARS-CoV-2-infected inpatients should consider these markers.
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COVID-19 , SARS-CoV-2 , Adulto , Humanos , Estudios Retrospectivos , Tratamiento Farmacológico de COVID-19 , BiomarcadoresRESUMEN
BACKGROUND & AIMS: Fatty liver disease (FLD) is common in women with polycystic ovary syndrome (PCOS). Here, we use non-invasive tests to quantify liver injury in women with PCOS and analyse whether FLD-associated genetic variants contribute to liver phenotypes in PCOS. METHODS: Prospectively, we recruited women with PCOS and controls at two university centres in Germany and Poland. Alcohol abuse was regarded as an exclusion criterion. Genotyping of variants associated with FLD was performed using TaqMan assays. Liver stiffness measurements (LSM), controlled attenuation parameters (CAP) and non-invasive HSI, FLI, FIB-4 scores were determined to assess hepatic steatosis and fibrosis. RESULTS: A total of 42 German (age range 18-53 years) and 143 Polish (age range 18-40 years) women with PCOS, as well as 245 German and 289 Polish controls were recruited. In contrast to Polish patients, Germans were older, presented with more severe metabolic profiles and had significantly higher LSM (median 5.9 kPa vs. 3.8 kPa). In the German cohort, carriers of the PNPLA3 p.I148M risk variant had an increased LSM (p = .01). In the Polish cohort, the minor MTARC1 allele was linked with significantly lower serum aminotransferases activities, whereas the HSD17B13 polymorphism was associated with lower concentrations of 17-OH progesterone, total testosterone, and androstenedione (all p < .05). CONCLUSIONS: FLD is common in women with PCOS. Its extent is modulated by both genetic and metabolic risk factors. Genotyping of variants associated with FLD might help to stratify the risk of liver disease progression in women suffering from PCOS.
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Enfermedad del Hígado Graso no Alcohólico , Síndrome del Ovario Poliquístico , Humanos , Femenino , Síndrome del Ovario Poliquístico/genética , Síndrome del Ovario Poliquístico/complicaciones , Factores de Riesgo , Enfermedad del Hígado Graso no Alcohólico/complicaciones , FenotipoRESUMEN
This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.
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COVID-19 , Humanos , COVID-19/diagnóstico por imagen , Inteligencia Artificial , Rayos X , Sensibilidad y Especificidad , RadiografíaRESUMEN
The study analyzes the correlation between the indications and results of head CT examinations in search of evidence of the excessive use of this diagnostic method. In total, 1160 referrals for urgent head CT were analyzed retrospectively, including the following parameters: patients' sex and age, type of scan (C-, C+, angio-CT), description of symptoms and presence of diagnostic target. Pathologies identified by the radiologist were assigned to four classes, regarding the severity of diagnosed conditions. The analysis of the CT results has shown that over half (55.22%) of the examinations revealed no deviations or showed chronic, asymptomatic lesions. As many as 73.71% referrals constituted group 0 in terms of the lack of a diagnostic target of a specific pathology. The presence of specific clinical targeting in a referral correlated significantly with a higher frequency of acute diagnosis. Contrast-enhanced follow-up examinations allowed the unequivocal classification of patients into extreme classes (I or IV) and accurate identification of patients requiring urgent or chronic treatment. Excessive use of diagnostic imaging is harmful, not only to patients, who often are unnecessarily exposed to radiation, but also to the quality of healthcare, since it increases the costs and radiologists' workload.
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Derivación y Consulta , Tomografía Computarizada por Rayos X , Humanos , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodos , Carga de TrabajoRESUMEN
Despite the growing popularity of mobile devices, they still have not found widespread use in medicine. This is due to the procedures in a given place, differences in the availability of mobile devices between individual institutions or lack of appropriate legal regulations and accreditation by relevant institutions. Numerous studies have been conducted and compared the usability of mobile solutions designed for diagnostic images evaluation on various mobile devices and applications with classic stationary descriptive stations. This study is an attempt to compare the usefulness of currently available mobile applications which are used in the medical industry, focusing on imaging diagnostics. As a consequence of the healthcare sector's diversity, it is also not possible to design a universal mobile application, which results in a multitude of software available on the market and makes it difficult to reliably compile and compare studies included in this systematic review. Despite these differences, it was possible to identify both positive and negative features of portable methods analyzing radiological images. The mobile application of the golden mean in hospital infrastructure should be widely available, with convenient and simple usage. Our future research will focus on development in the use of mobile devices and applications in the medical sector.
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Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) that mainly affects developed countries, but the number of cases in developing countries is increasing. We conducted a narrative review on the potential application of ultrasound elastography in the diagnosis and monitoring of UC, as this newly emerging method has promising results in other gut diseases. This review fulfilled the PRISMA Statement criteria with a time cut-off of June 2022. At the end of the review, of the 1334 identified studies, only five fulfilled all the inclusion criteria. Due to the small number of studies in this field, a reliable assessment of the usefulness of ultrasound elastography is difficult. We can only conclude that the transabdominal elastography examination did not significantly differ from the standard gastrointestinal ultrasonography examination and that measurements of the frontal intestinal wall should be made in the longitudinal section. The reports suggest that it is impossible to estimate the clinical scales used in disease assessment solely on the basis of elastographic measurements. Due to the different inclusion criteria, measurement methodologies, and elastographic techniques used in the analysed studies, a reliable comparative evaluation was impossible. Further work is required to assess the validity of expanding gastrointestinal ultrasonography with elastography in the diagnosis and monitoring of UC.
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Epilepsy is one of the most frequent serious brain disorders. Approximately 30,000 of the 150,000 children and adolescents who experience unprovoked seizures are diagnosed with epilepsy each year. Magnetic resonance imaging is the method of choice in diagnosing and monitoring patients with this condition. However, one very effective tool using MR images is volBrain software, which automatically generates information about the volume of brain structures. A total of 57 consecutive patients (study group) suffering from epilepsy and 34 healthy patients (control group) who underwent MR examination qualified for the study. Images were then evaluated by volBrain. Results showed atrophy of the brain and particular structures-GM, cerebrum, cerebellum, brainstem, putamen, thalamus, hippocampus and nucleus accumbens volume. Moreover, the statistically significant difference in the volume between the study and the control group was found for brain, lateral ventricle and putamen. A volumetric analysis of the CNS in children with epilepsy confirms a decrease in the volume of brain tissue. A volumetric assessment of brain structures based on MR data has the potential to be a useful diagnostic tool in children with epilepsy and can be implemented in clinical work; however, further studies are necessary to enhance the effectiveness of this software.
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The radiation dose during digital subtraction angiography (DSA) examination is determined on the basis of many factors. The aim of this study was to evaluate the dose-area product, air kerma, and fluoroscopy time on monoplane and biplane DSA of the cerebral arteries. Our results show that biplane angiography offered lower DAP, cumulative air kerma at the reference point and fluoroscopy time despite comparable procedure time.. Further research in this area and personnel training warrant the improvement of the radiosafety of angiographic interventional procedures and further development of angiographic technology.
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Estudios Retrospectivos , Angiografía de Substracción Digital/efectos adversos , Angiografía de Substracción Digital/métodos , Estudios Transversales , Fluoroscopía/efectos adversos , Fluoroscopía/métodos , Dosis de RadiaciónRESUMEN
Background: Complications of cystic fibrosis-associated liver disease (CFLD) are a leading nonpulmonary cause of death. Noninvasive tests enabling early detection of liver changes, especially in children are sought. The aim of the study was to assess the scale of liver fibrosis with the use of elastography in paediatric patients with diagnosed cystic fibrosis (CF) and its comparison with other tests (APRI and Fibrotest). Methods: We examined 41 children, in the age range 2-21 years, with diagnosed CF. The analysis a included clinical picture, laboratory parameters of liver damage, and cholestasis. Aspartate aminotransferase-to-platelet ratio index (APRI) and Fibrotest were done in all patients. Liver stiffness measurements were acquired using shear-wave elastography (SWE). Results: CFLD was diagnosed in 16/41 patients (39%). Abnormal elastography was observed in 19/41 patients (46.3%), and in 5/41 (12.2%), the changes were advanced (F4). Abnormal elastography was observed in 12/16 (75%) of the patients with CFLD, and in 7/25 (28%), there were no lesions observed in the liver in the course of cystic fibrosis. In all patients with F4, we observed abnormal results of the APRI and Fibrotest. In most patients with small changes in elastography, we found normal results of the APRI and Fibrotest. Conclusion: Elastography seems to be a noninvasive examination useful in everyday clinical work in detecting early liver changes and monitoring of progression in paediatric patients with diagnosed cystic fibrosis, ahead of changes in laboratory tests. The cost-effectiveness of this test, the possibility of its repetition, and its availability are additional benefits.