Normal Chest Research Articles

Biometric Data of Adults’ Aortic Knob Diameter in Posteroanterior Chest Radiograph, Correlation to Age and Normative Heart Diameter: A Cross-Sectional Study

Background: The aortic knob (AK) is an essential feature on a chest x-ray. It could be the first sign of a cardiovascular problem if there is any deformation or enlargement of the knob. This study aimed to measure the normal AK diameter (AKD) on a posteroanterior chest radiograph in healthy adult Sudanese. Methods and Results: The study was conducted in the Department of Radiology and Imaging in Ribat Hospital (Sudan) between Jun 2019 and Jan 2020. A total of 113 participants of both sexes (45.1% males and 54.9% females) with a normal chest x-ray and no history of diabetes, blood hypertension, cardiovascular disease, or skeletal abnormality were selected. Participants' age fluctuated from 18 to 75 years. The measurements (AK, heart diameter [HD], cardiothoracic ratio [CTR]) were carried out with the measuring tools available on the software of the computed radiography system. The mean AKD was 2.8±0.8 cm (2.94±0.8 cm in males and 2.51±0.77 cm in females, P=0.005)). The mean HD was 9.22±2.8 cm (9.8±3.3 cm in males and 8.7±0.2.1 cm in females, P=0.005). The mean CTR was estimated as 46.6±7.7% with a significant difference between males and females and significantly correlated with HD and BMI (P<0.05). The AKD increased by 0.019 cm with an increase of one year of age (AKD = 0.0199(age)+1.9469), and there was a strong positive correlation between age and AKD (P<0.001). Conclusion: The study found a significant positive correlation between age and AKD. Increased heart sizes increase AKD. The AKD value is greater in males than in females.

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.

Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

Thoracic ultrasound alone or in combination with tracheal amylase as a tool predictor of ventilator-associated pneumonia in neurocritical patients

In this study, we aim to evaluate whether thoracic ultrasound (TUS) and tracheal amylase (TA) alone or in combination can predict the development of ventilator-associated pneumonia (VAP) in neurocritical patients. Consecutive adult patients with neurocritical disease with normal chest radiographs who required intensive care unit admission and mechanical ventilation between March 2015 and July 2018 were included. TUS and Amylase levels were measured during the first 24 hours and repeated 48 hours after orotracheal intubation. Forty-three patients with a median age of 34 years (17-82) were included. TUS had a sensitivity of 100% and specificity of 96.3% as a predictor of VAP within the first 48 hours when nonpattern A was observed. TA levels > 200 UI/L in the first 48 hours had a sensitivity of 87.5%, and specificity of 63% as a predictor of VAP. Moreover, no benefit of TUS plus TA compared to TUS alone as a predictor of VAP was found. The identification of abnormal TUS patterns in the first 48 hours of orotracheal intubation is a significant predictor of VAP in neurocritical patients.

Comparison of pre-trained deep learning model classification performance of COVID-19 and normal chest X-ray images

Comparison of pre-trained deep learning model classification performance of COVID-19 and normal chest X-ray images

Sputum smear conversion and treatment outcomes among drug-resistant pulmonary tuberculosis patients in eastern Ethiopia: A 9-years data analysis.

Drug-resistant tuberculosis (DR-TB) has become a public health problem throughout the world and about one-third of deaths were attributed to DR-TB from antimicrobial resistance which contributes to 10% of all TB deaths. Sub-Saharan Africa, particularly Ethiopia accounts for a significant number of TB cases. However, the scanty evidence on DR-TB contributing factors could affect the level of this deadly case tackling program. Therefore, this study aimed to assess the factors affecting sputum smear conversion and treatment outcomes among patients with DR-TB in Health facilities in Eastern Ethiopia. A cross-sectional study design was employed from 10 October to 10 November 2021, in the health facilities providing DR-TB services in Harari Region and Dire Dawa city administration. The medical records of 273 DR-TB patients from 10 January 2013 to 27 December 2021, were reviewed using structured checklists. Data were entered into Epidata 3.1 version and exported to STATA 14 version for analysis. The outcome variables were Initial Sputum conversion (converted vs. not-converted) and treatment outcome (Unfavorable vs. Favorable). Sputum examination was performed using both Acid-fast bacillus (AFB) smear microscopy and Löwenstein-Jensen (LJ) culture technique. A binary logistic regression analysis was used to assess the association of independent variables with the first month sputum smear conversion, while a conditional logistic regression model was used to assess the association of treatment outcome with explanatory variables. The associations were reported using adjusted odds ratios (AORs) at a 95% confidence interval. A total of 273 DR-TB patients were included in this study. The unfavorable DR-TB treatment outcome was significantly associated with the history of chewing khat (AOR = 4.38, 95% CI = 1.62, 11.84), having bilateral lung cavity on baseline chest X-ray (AOR = 12.08, 95% CI = 1.80, 2.57), having greater than 2+ smear result at baseline (AOR = 3.79, 95% CI = 1.35, 10.59), and poor adherence (AOR = 2.9, 95% CI = 1.28, 6.82). The sputum smear non-conversion at first month was significantly associated with being Human Immune Virus (HIV)-negative (AOR = 0.37, 0.17, 0.82), having low baseline BMI (AOR = 0.54, 95% CI = 0.29, 0.97), baseline culture > 2++ (AOR = 0.15, 95% CI = 0.05, 0.49) and having greater than 2+ sputum smear result (AOR = 0.09, 95% CI = 0.012, 0.67). Patients with normal chest X-ray at baseline had 3.8 times higher chance of sputum smear conversion on first month (AOR = 3.77, 1.11, 12.77). The overall initial sputum smear conversion and the treatment success rate among DR-TB patients were 52.75 and 66.30%, respectively. The Baseline underweight, HIV-negative, baseline smear > 2+, baseline culture > 2++, and clear lung on baseline X-ray were associated with smear conversion and history of khat chewing, bilateral lung cavity at baseline, having greater than 2+ smear results at baseline, and patients with poor treatment adherence had hostile treatment outcomes. So, strengthening and implementing nutrition assessment and patient counseling during directly observed therapies (DOTs) service and drug compliance could result in early sputum conversion and better treatment outcomes. DR-TB patients with high bacterial load and abnormal lungs on radiologic examination at baseline could need special attention during their course of treatment.

Diagnosis challenges of a patient with peritoneal tuberculosis masquerading as ovarian malignancy: a case report

Background: Peritoneal tuberculosis (PTB) could mimic advanced ovarian cancer cases, making its diagnosis challenging. Comprehensive and conscientious examinations for diagnosis are needed to differentiate such cases to avoid inappropriate treatment. We report a problematic diagnosis of PTB that mimic ovarian malignancy. Case Presentation: A 20-year-old female patient with history of multiple rehospitalizations, presented with complaints of abdominal pain, nausea/vomiting, low-grade fever, and weight loss in the last five months. In the first admission, patient was suspected with malignant ovarian cyst due to clinical features of malignancy with normal chest X-ray, abdominal USG suggested ovarian cyst in both ovaries, high levels of Ca125. However abdominal CT-scan showed thickening in peritoneum which suggested PTB. The patient was planned for urgent laparotomy while waiting for GenExpert result. On the second admission, patient presented with recurrent abdominal pain, GenExpert sputum and stool showed negative result, and planned for surgery but the patient refused. On the last admission, patient diagnosed with impending bowel obstruction and underwent abdominal CT-scan with contrast showed diffuse multiloculated cystic lesion in cavum pelvic and multiple prominent mesenteric lymph nodes that suggested abdominal TB with abscess in cavum pelvic. Exploratory laparotomy revealed conglomeration, pus and tubercles in the peritoneum. A TB was confirmed based on pathology and microbiology examination from tissue biopsy and pus. Conclusion: We reported patients with PTB with unspecific clinical symptoms which suspected as ovarian malignancy. This study highlights the challenges in diagnosing PTB and therefore PTB could be considered a differential diagnosis in patients suspected of ovarian malignancy in the TB endemic countries.

Detection of COVID-19 infected lungs from chest x-ray using wavelet transform, HOG features extraction and SVM

Coronavirus SARS-CoV-2 referred to as COVID-19, is a both spreadable and infectious disease, which has footprinted a global pandemic and still infecting millions across the globe. At present, COVID-19 has made a devastating impact on our daily life. To detect coronavirus, some medical radiography technique is prominent such as chest X-ray images. This work represented the distinguishing features between normal and COVID-19 infected chest X-ray images through Discrete Wavelet transform (DWT) and Histogram of Oriented Gradients (HOG) methods which helps to indicate whether the person is COVID positive or negative. DWT and HOG transformations were performed to extract the features from the chest x-ray images. Support Vector Machine (SVM) classifier is used to the chest x-ray images for model training and validation. To evaluate the performance of the model accuracy, sensitivity, specificity and precision were calculated. DWT-SVM model provides the accuracy of 98.58%, the sensitivity of 98.38%, the specificity of 98.47% and the precision of 98.48% whereas the HOG-SVM model provides the accuracy of 99.39%, the sensitivity of 99.19%, the specificity 99.28% and precision 99.29%. So, the result indicates that the HOG-SVM model shows better performance than the DWT-SVM model. The experimental results may help the medical personnel to diagnose easily and to take the necessary steps for better treatment.

Comparison of Gray-scale Inversion to Improve Detection of Pulmonary Nodules on Chest X-rays Between Radiologists and a Deep Convolutional Neural Network

Detection of pulmonary nodules on chest x-rays is an important task for radiologists. Previous studies have shown improved detection rates using gray-scale inversion. The purpose of our study was to compare the efficacy of gray-scale inversion in improving the detection of pulmonary nodules on chest x-rays for radiologists and machine learning models (ML). We created a mixed dataset consisting of 60, 2-view (posteroanterior view - PA and lateral view) chest x-rays with computed tomography confirmed nodule(s) and 62 normal chest x-rays. Twenty percent of the cases were separated for a testing dataset (24 total images). Data augmentation through mirroring and transfer learning was used for the remaining cases (784 total images) for supervised training of 4 ML models (grayscale PA, grayscale lateral, gray-scale inversion PA, and gray-scale inversion lateral) on Google's cloud-based AutoML platform. Three cardiothoracic radiologists analyzed the complete 2-view dataset (n=120) and, for comparison to the ML, the single-view testing subsets (12 images each). Gray-scale inversion (area under the curve (AUC) 0.80, 95% confidence interval (CI) 0.75-0.85) did not improve diagnostic performance for radiologists compared to grayscale (AUC 0.84, 95% CI 0.79-0.88). Gray-scale inversion also did not improve diagnostic performance for the ML. The ML did demonstrate higher sensitivity and negative predictive value for grayscale PA (72.7% and 75.0%), grayscale lateral (63.6% and 66.6%), and gray-scale inversion lateral views (72.7% and 76.9%), comparing favorably to the radiologists (63.9% and 72.3%, 27.8% and 58.3%, 19.5% and 50.5% respectively). In the limited testing dataset, the ML did demonstrate higher sensitivity and negative predictive value for grayscale PA (72.7% and 75.0%), grayscale lateral (63.6% and 66.6%), and gray-scale inversion lateral views (72.7% and 76.9%), comparing favorably to the radiologists (63.9% and 72.3%, 27.8% and 58.3%, 19.5% and 50.5%, respectively). Further investigation of other post-processing algorithms to improve diagnostic performance of ML is warranted.

Abstract 14091: Severe Hypothyroidism Revealing Hidden Tracts

Discussion: Atrioventricular reciprocating tachycardia (AVRT) is a type of Supraventricular tachycardia (SVT) caused by an accessory pathway can result in symptomatic and also life-threatening arrhythmias. Supraventricular tachycardias are less commonly seen in hypothyroidism than hyperthyroidism. We present a case of a 41 year old gentleman who presented with chest pain and SVT was found to have severe hypothyroidism with pre-excitation pattern on his EKG. Case: 41-year-old Caucasian gentleman with history of untreated Hashimoto’s thyroiditis presented with palpitations and acute onset left-sided chest pain. His symptoms were aggravated with movement and improved with rest. When EMS found him, his vitals were significant for tachycardia with a heart rate (HR) of 200 beats per minute (bpm). EKG showed supraventricular tachycardia. Vagal maneuvers were attempted followed by adenosine. This resulted in conversion to sinus rhythm and improvement in HR to 88 bpm. His chest pain completely resolved. He was then brought to the emergency room where he had normal vitals. Examination revealed thyroid gland was palpable and noted to be diffusely enlarged. Laboratory data was significant for an elevated thyroid-stimulating hormone (TSH) of 373 mIU/ml and undetectable free thyroxine (fT4) levels. His creatinine kinase (CK) was high at 16260, and troponin was elevated at 0.220 ng/ml (reference:<0.04 ng/ml). TPO antibodies were >600 IU/ml. EKG demonstrated sinus rhythm with pre-excitation and incomplete left bundle-branch block. He had normal chest x-ray. An echocardiogram showed an ejection fraction of 55% with mild LV diastolic dysfunction. He later underwent a stress myocardial perfusion scan which showed no definite evidence of inducible ischemia. Follow up: He was started on thyroxine replacement therapy. He is due for an EP study to evaluate for an accessory pathway causing pre-excitation with repolarization pattern on his ECG. Conclusion: This case suggests that severe hypothyroidism can also cause supraventricular tachycardias (SVT) by metabolic and conduction derangements revealing concealed bypass tracts in patients with accessory pathways. In most cases these arrhythmias are reversible once the underlying thyroid disorder is treated.

Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study.

In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. Methods: A team of radiologists used the AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India between June 2021 and March 2022. Results: A total of 65,604 chest X-rays were processed during the study period. The overall performance of AI achieved in detecting normal and abnormal chest X-rays was good. The high negatively predicted value (NPV) of 98.9% was achieved. The AI performance in terms of area under the curve (AUC), NPV for the corresponding subabnormalities obtained were blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, 99.9%), rib fracture (0.98, 99.9%), scoliosis (0.98, 99.9%), atelectasis (0.96, 99.9%), calcification (0.96, 99.7%), consolidation (0.95, 99.6%), emphysema (0.96, 99.9%), fibrosis (0.95, 99.7%), nodule (0.91, 99.8%), opacity (0.92, 99.2%), pleural effusion (0.97, 99.7%), and pneumothorax (0.99, 99.9%). Additionally, the turnaround time (TAT) decreased by about 40.63% from pre-qXR period to post-qXR period. Conclusions: The AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.

COVID-19 detection based on pre-trained deep networks and LSTM model using X-ray images enhanced contrast with artificial bee colony algorithm.

Coronavirus (COVID-19) is an infectious disease that has spread across the world within a short period of time and is causing rapid casualties. The main symptoms of this virus are shortness of breath, fever, cough, and a sore throat. The virus is detected through samples, such as throat swabs and sputum, taken from people who meet the possible case definition and the results are usually obtained within a few hours or a day. The development of test kits to detect the COVID-19 virus is still an open research topic, and automated and faster diagnostic tools are needed. Recent studies have shown that biomedical images can be used for COVID-19 testing. This study proposes the hybrid use of pre-trained deep networks and the long short-term memory (LSTM) for the classification of COVID-19 from contrast-enhanced chest X-rays. In the proposed system, a transformation function is applied to X-ray images first. Then, the artificial bee colony (ABC) algorithm is used to optimize the parameters obtained from the transformation function. The pre-trained deep network models and LSTM are preferred to extract features from the contrast-enhanced chest X-rays. At the final stage, COVID-19, normal (healthy), and pneumonia chest X-ray are classified using softmax. To evaluate the performance of the proposed method, the "COVID-19 radiography" dataset, which is widely used in the literature, is preferred. From the proposed model, 98.97% accuracy, 98.80% precision, and 98.70% sensitivity rates are obtained. Experimental results reveal that the proposed model provides efficient results compared to other methods. Thanks to the application of ABC-based image enhancement, increased classification of 2.5% has been achieved against other state-of-the-art models.

ERYTHRODERMA AND EOSINOPHILIA WITH MULTI-SYSTEM INVOLVEMENT, A CASE OF VRESS

<h3>Introduction</h3> Resembling Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS), Virus Reactivation with Eosinophilia and Systemic Symptoms (VRESS) occurs triggered by members of Herpesviridae family viruses. <h3>Case Description</h3> A 12-year-old male was hospitalized with a 5-day history of fever, pruritic erythematous rash on the trunk and extremities, and facial edema. The patient was exposed to a cousin diagnosed with Multisystem Inflammatory Syndrome in Children (MIS-C) 10 days prior. No history of traveling, unusual food intake, URI, drug ingestion during the prior 3 months. Patient was treated with clindamycin x 10 days for possible Staphylococcal Scalded Skin Syndrome. Patient was noted to have eosinophilia of >2000 that persisted through the hospital course and thereafter, transaminitis with direct bilirubinemia, transient microscopic hematuria, elevated CRP and C3, normal EKG and chest radiograph, normal T/B/NKC counts, normal levels of immunoglobulins and elevated IgE (2163>1579IU/mL). IgG and IgM were negative for: CMV, adenovirus, strongyloidces, HAV, HBV, and HIV. IgG positive to: EBV, HHV-6, mycoplasma, HHV-6, HSV1, and parvovirus. Negative cultures for MRSA and group A Streptococcus. Negative PCRs for COVID -19. At 8 week follow up, skin erythema evolved into desquamation in the fingers and eosinophilia persisted. <h3>Discussion</h3> VRESS should be on the differential for patients presenting with a DRESS-like symptoms in the absence of drug exposures. VRESS is often triggered by members of the Herpesviridae family of viruses including EBV and HHV-6, to both of which this patient was exposed to in the past. While diagnosis is one of exclusion, early identification can guide appropriate management.

Robotic-Assisted Laparoscopic Treatment of Diaphragmatic Endometriosis

<h3>Study Objective</h3> To demonstrate the surgical technique used for a robotic-assisted diaphragm endometriosis treatment. <h3>Design</h3> Case report illustrated with a video. <h3>Setting</h3> Patient is placed in the semi-gynecological position, left lateral decubitus and proclivity. Robotic side docking set for upper abdomen using da Vinci Si platform. <h3>Patients or Participants</h3> A 34-year-old woman reports dysmenorrhea and pain in her right shoulder during menstruation for 5 years, ceased after combined oral contraceptive use. When she stopped the contraceptive, symptoms recurred. On MRI, hematic foci were identified in the right subphrenic space, suggesting endometrial implants. <h3>Interventions</h3> A 12 mm incision is performed in the umbilicus for endoscope port. Other two 8mm robotic ports and a 5 mm accessory laparoscopic trocar were placed. Patient's cart side docking enters obliquely through patient's left shoulder. Inspection shows superficial and deep typical endometriotic lesions on right diaphragmatic surface, falciform ligament is sectioned for better liver mobilization. Most superficial lesions are removed before accessing thoracic cavity. For deeper lesions, the diaphragm is opened, showing pleural involvement of endometriosis. No pulmonary lesions were identified. Diaphragm is repaired carried out by a double-layered barbed suture. Before first layer closure, a laparoscopic suction tip is introduced into the thoracic cavity and the aspiration of the pneumothorax is done concomitantly with the forced Valsalva maneuver. <h3>Measurements and Main Results</h3> Patient was discharged in the second postoperative day, showing great evolution in respiratory physical therapy and normal chest X-ray. Painful shoulder symptoms have completely resolved. <h3>Conclusion</h3> Diaphragm endometriosis has been gaining more attention in the practice of gynecologist surgeons. Robotic system improves access to the subdiaphragmatic space due to the articulated instrument movements and 3D high-definition static vision. Excision of diaphragmatic lesions is a standardized feasible procedure that allows complete treatment and good clinical results.

Burden of active pulmonary tuberculosis among patients with diabetes in Dar es Salaam, Tanzania: a cross-sectional study

ObjectivesWe aimed to determine the prevalence, associated factors and describe the chest radiographic findings for active pulmonary tuberculosis (TB) among patients with diabetes mellitus (DM) attending a diabetic clinic in...

Detection of COVID-19 Cases Based on Deep Learning with X-ray Images

Since the outbreak of COVID-19, the coronavirus has caused a massive threat to people’s lives. With the development of artificial intelligence technology, identifying key features in medical images through deep learning, infection cases can be screened quickly and accurately. This paper uses deep-learning-based approaches to classify COVID-19 and normal (healthy) chest X-ray images. To effectively extract medical X-ray image features and improve the detection accuracy of COVID-19 images, this paper extracts the texture features of X-ray images based on the gray level co-occurrence matrix and then realizes feature selection by principal components analysis (PCA) and t-distributed stochastic neighbor embedding (T-SNE) algorithms. To improve the accuracy of X-ray image detection, this paper designs a COVID-19 X-ray image detection model based on the multi-head self-attention mechanism and residual neural network. It applies the multi-head self-attention mechanism to the residual network bottleneck layer. The experimental results show that the multi-head self-attention residual network (MHSA-ResNet) detection model has an accuracy of 95.52% and a precision of 96.02%. It has a good detection effect and can realize the three classifications of COVID-19 pneumonia, common pneumonia, and normal lungs, proving the method’s effectiveness and practicability in this paper.

Rare findings in ectopic breast tissue: A case study of 7 patients

BackgroundEctopic Breast Tissue is a rare condition; it can be present anywhere along the milk line during adult life. Although rare, pathological conditions including breast carcinoma or benign breast disease can occur in Ectopic breast tissue. AimIdentify the most proper approach to the management of malignant conditions affecting ectopic breast tissue. Patients and methodsA prospective cohort study including 7 patients who presented with a mass in their Ectopic Breast Tissue visited the breast clinic and general surgery clinic at Al-Yarmouk teaching hospital for a period of five years. All the patients were subjected to triple assessment. Treatment was done according to the findings. All patients were followed for 1–2 years. ResultFive patients complained of a painful mass (3 in the axilla and 2 were infra-mammary). The remaining two cases were painless axillary mass. Histopathology and cytology revealed that 1 patient presented with fibroadenoma, 5 patients had Invasive ductal carcinoma, and 1pateint was diagnosed with Invasive lobular carcinoma. Modified radical mastectomy was done in 3 cases, wide local excision with axillary clearance in 2 cases, and wide local excision alone for 2 patients. Distant metastasis occurs in 2 cases of Modified radical mastectomy, in addition to local and distant metastasis in 1 patient with wide local excision alone. ConclusionIn ectopic breast carcinoma, modified radical mastectomy is indicated only in the presence of concurrent ipsilateral breast carcinoma and when there is an extension to the normal pectoral breast or chest wall. Otherwise, wide local excision with axillary clearance seems to be sufficient treatment with no recurrence or survival rate difference.

Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography

IntroductionWith the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized techniques for diagnosing and handling Covid-19 cases. However, strong detection tools can help avoid damage to chest tissues. The proposed method provides an important way to enhance the semantic segmentation process using combined potential deep learning (DL) modules to increase consistency. Based on Covid-19 CT images, this work hypothesized that a novel model for semantic segmentation might be able to extract definite graphical features of Covid-19 and afford an accurate clinical diagnosis while optimizing the classical test and saving time. MethodsCT images were collected considering different cases (normal chest CT, pneumonia, typical viral causes, and Covid-19 cases). The study presents an advanced DL method to deal with chest semantic segmentation issues. The approach employs a modified version of the U-net to enable and support Covid-19 detection from the studied images. ResultsThe validation tests demonstrated competitive results with important performance rates: Precision (90.96% ± 2.5) with an F-score of (91.08% ± 3.2), an accuracy of (93.37% ± 1.2), a sensitivity of (96.88% ± 2.8) and a specificity of (96.91% ± 2.3). In addition, the visual segmentation results are very close to the Ground truth. ConclusionThe findings of this study reveal the proof-of-principle for using cooperative components to strengthen the semantic segmentation modules for effective and truthful Covid-19 diagnosis. Implications for practiceThis paper has highlighted that DL based approach, with several modules, may be contributing to provide strong support for radiographers and physicians, and that further use of DL is required to design and implement performant automated vision systems to detect chest diseases.

Normative Value of Chest Expansion in Healthy Children Between 5 to 12 Years of Age Group

Background: Chest expansion measurement is included as a standard mode of measurement to evaluate patient’s baseline status of respiratory function. There are various anatomical and physiological differences between adult and paediatric respiratory system hence, adult values of chest expansion cannot be used as reference values in paediatric population. Also, there is lack of recent data that has evaluated chest expansion values in paediatric age group. So, the present study was undertaken to find out normal chest expansion values in children between 5-12 years of age. It is important to know the reference value for precise interpretation. Methodology: A cross sectional observational study was carried out in 600 children between age group of 5-12 years and with normal BMI(without H/O any cardiac/respiratory illness, musculoskeletal or neurological diseases, spinal deviations, cough and cold). Chest expansion measurements were assessed in sitting position at three levels that is, 2nd, 4th and 6th intercostal spaces(ICS), using an non- elastic measuring tape. It was taken as thoracic circumference at the end of maximum exhalation and inspiration. An average of three such readings at each level was taken into consideration. Results: To find value of chest expansion at 2nd, 4th and 6th ICS levels a average values of the three measurements taken at each level at same time was taken into consideration. Conclusion: The normative values of chest expansion in healthy children between age group of 5-12 years is 2nd Intercostal space- 1 inch/ 2.5 cm, 4th Intercostal space- 1 inch/2.5 cm, 6th Intercostal space- 1.7 inch/ 4.25cm. KEY WORDS: Chest Expansion, Normative Values, Children, Paediatric.

41 Twenty years’ experience with juvenile dermatomyositis among Libyan children

BackgroundJuvenile inflammatory myositis are systemic autoimmune diseases of unknown aetiology that are characterized by inflammation of skeleton, muscles, skin, and internal organs. Studies from different regions have reported different incidence and age at onset suggesting that the clinical and demographic features may differ by race and geographic regions. This study aims to describe the characteristics of disease among Libyan children who have been treated in the main rheumatology unit covering most of the population in LibyaObjectivesTo describe the demographic and clinical features of patients with juvenile dermatomyositisTo determine the outcome of children with juvenile dermatomyositis and factors affecting the outcome.Patients and methodsThis is a retrospective descriptive study conducted by reviewing patient records diagnosed with juvenile dermatomyositis from 5/2000–2/2022.ResultsTwenty-one patients were included, female to male ratio was 6:1. Mean age at disease onset was 7.7 ± 2.8 years and their mean follow-up period was 4.36 ± 3.2 years. Most of the patients (14.7%) presented before 1 month of starting symptoms. Most of the patients (13,0.9%), were diagnosed as dermatomyositis, 2 (9.5%) as polymyositis, 2 (9.5%) as amyopathic myositis, and 4 (19%) as overlap syndrome. Family History of dermatomyositis was positive in 4 (19%) of patients and family history of other autoimmune diseases was positive in 5 (23.8%) patients.Around half of the patients had monocyclic disease course (11, 52.4%), 5 patients (23.8%) had polycyclic disease course and other 5 patients (23.8%) had chronic persistent disease course.MRI proximal muscles and EMG were used for diagnosis in 10 patients (48%) and 16 (76.1%) respectively, and muscle biopsy was used in 2 patients to confirm the diagnosis.One overlap patient had severe lung fibrosis on CT scan chest with restrictive lung disease and 4 patients (19%) had restrictive lung disease with normal CT scan chest.Methyleprednisolone pulses were used in 10 patients (50%) with 3 (15%) who required more than one pulse of intravenous prednisolone. Immunoglobulin was used in 12 (60%) of the patients. All patients needed oral prednisolone and methotrexate was used in 15 (75%) of the patients. Other drugs used were Azathioprine in 3 overlap patients, cyclophosphamide and Mycophenolate mofetil in one patient with overlap syndrome. Hydroxychroroquine was used in 6 (30%) of the patients.In the last visit, 12 patients (60%) were in remission, 7 (58%) of them presented before 1 month of disease onset. Fifty-eight percent of the females and 66.7% of the males were in remission. Five patients (25%) had short stature, 3 (15%) had chronic cutaneous changes, 1 (5%) had arrythmia, 2 (10%) had calcinosis, 2 (10%) had osteoporosis, and one patient died.ConclusionA female predominance was noted with age at presentation comparable to other studies.The most frequent manifestations were skin manifestations and musculoskeletal features were the second most frequent symptoms.Patients has low rate of complications with low rate of calcinosis and mortality and none of them had gastrointestinal manifestations.Males were more likely to be in remission. 41 Table 1summarizes the main clinical features at presentation:FeatureNumber and percentageSkin changes17 (81%)Facial oedema1 (4.8%)Hands and feet oedema1 (4.8%)Fever4 (19%)Generalized fatigability12 (57.1%)Arthritis11 (52.4%)Arthralgia1 (4.8%)Muscle weakness15 (71%)Dysphonia4 (19%)Contractures2 (9.5%)

A CASE OF AUTOSOMAL-DOMINANT HYPER-IGE SYNDROME MASQUERADING AS CYSTIC FIBROSIS

A CASE OF AUTOSOMAL-DOMINANT HYPER-IGE SYNDROME MASQUERADING AS CYSTIC FIBROSIS