Abnormal Chest Radiograph Research Articles

Analysis of factors affecting health status and abnormal lung function of workers exposed to welding fumes in Tianjin from 2020 to 2022

Objective: To investigate the health status and the influencing factors of abnormal lung function of welding fumes exposure workers in Tianjin from 2020 to 2022, and to provide theoretical basis for the protection and intervention of key occupational diseases. Methods: In October 2023, the relevant data of occupational health examination of welding fumes exposure workers in Tianjin from 2020 to 2022 (10831, 10758 and 21717 in 2020, 2021 and 2022, respectively) were collected from the China Disease Prevention and Control Information Platform. The electrocardiogram, chest radiograph and lung function abnormalities of workers in each year were analyzed, as well as the association between lung function abnormalities and gender, age, working age, region, etc. With gender, age and years of service as independent variables, logistic regression analysis was performed year by year on the influencing factors of abnormal lung function. Results: From 2020 to 2022, electrocardiogram, chest radiograph and lung function abnormality rates of workers exposed to welding fumes had statistical significances (χ(2)=301.79, 7.87, 1614.54, P<0.05), and the lung function abnormality rate showed a downward trend year by year (χ(2)(trend)=1516.19, P<0.001). The abnormal rates of lung function in male workers exposed to welding fumes were higher than those in female workers (χ(2)=61.65, 8.04, 15.22, P<0.05). With the increase of age, the abnormal rate of lung function showed an increasing trend (χ(2)(trend)=6.47, 10.81, 4.72, P<0.05). The abnormal rates of lung function in the four districts around the city in each year were 22.05% (318/1442), 8.91% (171/1919) and 6.48% (273/4212), respectively. Year by year logistic regression analysis showed that females were the protective factors for abnormal lung function (OR=0.369, 0.568, 0.546; 95%CI: 0.298-0.458, 0.367-0.879, 0.388-0.767; P<0.05) . Conclusion: Attention should be paid to the health status of welding fumes workers in Tianjin, focusing on the protection of middle-aged male workers, and effective prevention measures should be taken for key areas and industries such as the four districts around the city.

Predictive value of clinico-radiological variables in the management of suspected foreign body aspiration in children: A ten-year retrospective cohort study

ABSTRACT Background: Definite history is not always present in children with foreign body aspiration (FBA), hence necessitating a high index of suspicion. Objective: To assess the predictive value of clinico-radiological variables among children presenting with features of suspected FBA and to document their course in a tertiary care teaching hospital. Materials and Methods: In this retrospective observational study, we included children aged below 15 years presenting with clinical features of suspected FBA. Data was obtained from case records. Multivariable binary logistic regression analysis (MVA) was performed to determine statistically significant predictors of FBA, and Weighted Risk Scores (WRS) were calculated to determine the significance of the model. Results: Of the 296 children included in the study and having undergone bronchoscopy, 84.5% were toddlers; 269 (90.87%) had a foreign body (FB) removed. Organic material was commonly found with 64.3% being peanut and right main bronchus, the commonest lodging site (50.6%). Of the 54 (18%) children with symptomatic but unwitnessed FBA, 45 (83.3%) had a FB retrieved. Choking with sudden onset cough, stridor, unilateral decreased breath sounds, abnormal chest radiograph, unilateral hyperinflation and mediastinal shift correlated with FBA (P &lt; 0.05). MVA confirmed statistical significance with choking with sudden onset cough to predict FBA (P = 0.024) with overall sensitivity and specificity of 60.9% and 77.8%, respectively. Total WRS with cut-off &gt;2 showed good power of discrimination with sensitivity, specificity and AUC of 60.2%, 77.8% and 0.704%, respectively. Conclusions: The proposed predictive value-based scoring system enhances accuracy in decision-making regarding bronchoscopy in children with suspected FBA.

Airway exploration when suspected foreign body aspiration. Is it always indicated?

BackgroundForeign body aspiration (FBA) is an important cause of morbidity and mortality in pediatrics. Flexible or rigid bronchoscopy is the gold standard for its diagnosis and treatment. However, despite being a minimally invasive test, it is not without risk and, given the low specificity of symptoms involved, between 45 and 70 % of diagnostic bronchoscopies (DB) performed are normal. The aim of this paper is to develop a strategy in order to reduce this percentage. MethodsObservational, analytical, and retrospective study of DB performed for suspected FBA between 2018 and 2022. Demographic, clinical, and radiological data, bronchoscopy findings and complications were obtained. Results96 patients admitted to DB were analyzed. 72 out of 96 (75 %) were normal. The risk of presenting a foreign body was significantly higher in patients with suspected nuts, so DB should be always indicated. In patients with suspected other materials aspiration, a multivariate analysis was performed using logistic regression. Statistically significant association was found between dyspnea, asymmetric auscultation and abnormal chest radiography with the presence of a foreign body on the DB; AUC = 0.97 (95 % CI 92.4 %-100 %). The proposed algorithm establishes patients at high risk of FBA and therefore the DB is recommended, with a sensitivity of 100 % and a specificity of 87.2 %. ConclusionsThe application of the algorithm to patients with suspected aspiration of a foreign body other than nuts can facilitate the identification of patients with high risk of aspiration and reduce the percentage of negative DB.

Comparison of Clinical Features and Outcomes of Shocks in Multisystem Inflammatory Syndrome in Children Associated with COVID-19 (MIS-C), Septic Shock, and Cardiogenic Shock

Background: Multisystem inflammatory syndrome in children associated with COVID-19 (MIS-C) is an inflammatory disorder that shares similarities with Kawasaki disease and toxic shock syndrome. The majority of MIS-C patients exhibit cardiovascular abnormalities, with cardiogenic and vasodilatory shock occurring in 60% of cases. Objectives: To determine the differences between MIS-C shock, septic shock, and cardiogenic shock. Methods: This retrospective observational study was conducted between January 2021 and December 2022. The study included children aged between 1 month and 15 years with MIS-C shock, septic shock, and cardiogenic shock. Patients’ medical records were reviewed, including clinical presentations, laboratory results, treatments, and outcomes. Results: The study included 60 patients: 13 (22%) with MIS-C shock, 33 (55%) with septic shock, and 14 (23%) with cardiogenic shock. Compared to septic shock and cardiogenic shock, MIS-C patients were more likely to present with a rash (P &lt; 0.001). Multisystem inflammatory syndrome in children shock patients had significantly lower lymphocyte counts (P = 0.001). Chest radiograph and echocardiography abnormalities were more common in MIS-C shock and cardiogenic shock patients than in septic shock patients (P = 0.004). USCOM results indicated that MIS-C shock patients displayed a combination of a decreased inotropic index and systemic Vascular Resistance Index. Conclusions: The most distinct clinical feature of MIS-C shock is a cutaneous manifestation combined with higher levels of inflammation and a decreased lymphocyte count, caused by vasodilation and myocardial dysfunction.

Salvage surgery following tyrosine kinase inhibitor treatment for advanced non-small cell lung cancer

BackgroundNo standard therapy for non-small lung cancer patients that have acquired resistance to tyrosine kinase inhibitor (TKI) therapy has been established. Some can be effectively treated by salvage surgery, though indications for that procedure remain unclear. Reported here is the clinical course of a patient who experienced early post-operative distant metastases.Case presentationA 48-year-old woman without symptoms was referred to another hospital for abnormal chest radiography findings and diagnosed with adenocarcinoma of the left lower lobe (cT2aN3M1b, stage IVB; TNM staging 7th edition). Gene mutation analysis revealed positive for epidermal growth factor receptor exon 19 deletion. Afatinib treatment was started, resulting in partial response, though regrowth of the main tumor was noted 1.5 years later. Bronchoscopic re-biopsy findings revealed a T790M point mutation and afatinib was switched to osimertinib. At 1.5 years following the start of osimertinib administration, the primary tumor was found to have regrown again and stereotactic radiation therapy was administered. Findings at 3.5 years after osimertinib administration indicated that all lymph nodes and distant metastases, excluding the primary tumor, were well controlled, and the patient was referred to our hospital for salvage surgery. Osimertinib was discontinued, and a left lower lobectomy with a left lingular segmentectomy and pleural biopsy were performed. The patient was discharged following an uneventful postoperative course. Three days after discharge, glossodynia developed and examination findings revealed tongue metastasis. The symptoms improved following re-administration of osimertinib, though right adrenal gland metastasis appeared 8 months after surgery. Radiation therapy was performed for tongue and right adrenal gland metastases, and the patient was alive 1 year after salvage surgery without out-of-control lesion appearing after the radiation therapy under the administration of osimertinib.ConclusionThe present patient experienced multiple instances of systemic recurrence after undergoing salvage surgery. Experience with this case indicates that systemic therapy is essential for patients with distant metastatic lung cancer even following salvage surgery for the primary tumor.

Diagnostic performance of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) in bronchoalveolar lavage fluid for pulmonary tuberculosis in HIV-infected patients

IntroductionPulmonary tuberculosis (PTB) remains a significant health concern, particularly in individuals infected with human immunodeficiency virus (HIV) who are more susceptible to developing active TB disease. Early and accurate diagnosis of TB is crucial for effective treatment and prevention of transmission. This study aims to evaluate the potential of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOFMS) analysis of bronchoalveolar lavage fluid (BALF) for diagnosis of suspected PTB in HIV-infected patients. MethodsThis retrospective study recruited 60 HIV-infected patients with suspected PTB presenting with respiratory symptoms and abnormal chest radiographs between January 2022 and June 2023. BALF samples were collected and subjected to analysis using MALDI-TOF MS, GeneXpert, acid-fast bacilli (AFB) smear and culture. And their diagnostic performance was compared. ResultsThe sensitivity of MALDI⁃TOFMS for diagnosing PTB was 83.3 %, which was better than that of smear 11.9 %, culture 40.5 % or Xpert38.1 % (all p < 0.01). The area under the curve (AUC) value of MALDI⁃TOFMS was 0.889, which was better than that of smear 0.532, culture 0.675 or Xpert 0.690 (all p < 0.01). The katG315 and rpoB-RRDR 511 mutations were detected by the MALDI⁃TOFMS in two patients. ConclusionNucleotide MALDI-TOFMS has a good clinical performance for rapid diagnosis of PTB from BALF samples in HIV infected patients, and detects mutations of TB simultaneously.

Incidence and risk factors of active tuberculosis among older individuals with latent tuberculosis infection: a cohort study in two high-epidemic sites in eastern China.

The influencing factors of the process from latent tuberculosis infection (LTBI) to the onset of active tuberculosis (TB) remain unknown among different population groups, especially among older individuals in high-incidence areas. This study aimed to investigate the development of active TB among older adults with LTBI and identify groups in greatest need of improved prevention and control strategies for TB. In 2021, we implemented an investigation among older individuals (≥ 65 years old) in two towns in Zhejiang Province with the highest incidence of TB. All participants underwent assessment using standardized questionnaires, physical examinations, interferon-gamma release assays, and chest radiography. All the participants with suspected TB based on the clinical symptoms or abnormal chest radiography results, as well as those with LTBI, were referred for diagnostic investigation in accordance with the national guidelines. Those with an initial diagnosis of TB were then excluded, whereas those with LTBI were included in a follow-up at baseline. Incident patients with active TB were identified from the Chinese Tuberculosis Management Information System, and a multivariate Cox regression model was used to estimate the incidence and risk of TB among those with LTBI. In total, 667 participants with LTBI were followed up for 1,315.3 person-years, revealing a disease density of 1,292.5 individuals/100,000 person-years (17/1,315.3). For those with LTBI, chest radiograph abnormalities had adjusted hazard ratios for active TB of 4.9 (1.6-15.3). The presence of abnormal chest radiography findings increased the risk of active TB among older individuals with LTBI in high-epidemic sites in eastern China.

Faces of tuberculosis: a riveting exploration into demographics and health profiles of patients in rural Jharkhand

Background: Tuberculosis is a pressing health concern in Jharkhand, India, claiming 13,000 lives annually. This retrospective study, conducted by Tata steel foundation, delves into the health and sociodemographic profiles of TB patients in the tribal-dominated East Singhbhum district. Noteworthy findings include risk factors such as Anaemia, Diabetes compounded by challenges like limited access to health facilities. TSF's initiatives, including Nikshay Mitra and support for NTEP, are crucial components in addressing TB in the region. Methods: A retrospective study (September to November 2023) analyzed records from TSF's DOTS centre in East Singhbhum. Data from 2019-2023 were extracted and analyzed using descriptive statistics. Results: The average age of 293 patients was 37.36±14.97 years, with 83.6% belonging to Scheduled Tribes. Sputum analysis revealed a 61.4% positive rate for Acid Fast Bacillus. Notably, 85.3% completed treatment, and abnormal chest radiographs were observed in 99.3% of cases. Conclusions: The proposed way forward outlines actionable strategies to further strengthen TB awareness, focus on challenging districts, engage communities, and collaborate with government health centers. Regular research and monitoring, coupled with periodic impact assessments, will be crucial for adapting and refining interventions in alignment with the dynamic landscape of TB prevalence.

Postoperative pulmonary complications in patients undergoing upper abdominal surgery: risk factors and predictive models.

Postoperative pulmonary complications (PPCs) are unexpected disorders that occur up to 30 days after surgery, affecting the patient's clinical status and requiring therapeutic intervention. Therefore, it becomes important to assess the patient preoperatively, as many of these complications can be minimized with proper perioperative strategies following a thorough preoperative checkup. Herein, we describe the PPCs and risk factors associated with developing PPCs in patients undergoing upper abdominal surgery. Additionally, we compared the accuracy of the American Society of Anaesthesiologists (ASA) score, the Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score, the 6-Minute Walk Test (6MWT), and spirometry in predicting PPCs. Consenting patients (>18 years) undergoing elective upper abdominal surgery were recruited from November 2021 to April 2023. Clinical history was noted. Spirometry and 6MWT were both performed. Pre-operative ASA and ARISCAT scores were recorded. Postoperative follow-up was conducted to assess respiratory symptoms and the occurrence of PPC. PPC was defined as per EPCO guidelines. A total of 133 patients were recruited, predominantly male. A total of 27 (20.3%) patients developed PPCs. A total of 14 (10.5%) patients had more than one PPC. The most common PPCs developed were pleural effusion (11.3%), respiratory failure (7.5%), and pneumonia (4.5%). We obtained ten statistically significant associated variables on univariable analysis, viz obstructive airway disease (p=0.002), airflow limitation (p=0.043), chest radiography (p<0.001), albumin (p=0.30), blood urea nitrogen (BUN) (p=0.029), aspartate aminotransferase (p=0.019), alanine aminotransferase (p=0.009), forced expiratory volume in one second/forced vital capacity ratio (p=0.006), duration of surgery (p<0.001), and ASA score (p=0.012). On multivariable regression analysis, abnormal chest radiograph [odds ratio: 8.26; (95% confidence interval: 2.58-25.43), p<0.001], BUN [1.05; (1.00-1.09), p=0.033], and duration of surgery [1.44; (1.18-1.76), p<0.001] were found to be independently associated with PPC. The ASA score was found to have better predictive power for the development of PPCs compared to the ARISCAT score but is of poor clinical significance. Additionally, 6MWD and spirometry results were found to lack any meaningful predictive power for PPC. To conclude, preoperative evaluation of the chest radiograph, BUN, and duration of surgery are independently associated with developing PPCs. The ASA score performs better than the ARISCAT score in identifying patients at a higher risk of developing PPCs and implementing preventive measures.

Step into the era of large multimodal models: a pilot study on ChatGPT-4V(ision)'s ability to interpret radiological images.

The introduction of ChatGPT-4V's 'Chat with images' feature represents the beginning of the era of large multimodal models (LMMs), which allows ChatGPT to process and answer questions based on uploaded images. This advancement has the potential to transform how surgical teams utilize radiographic data, as radiological interpretation is crucial for surgical planning and postoperative care. However, a comprehensive evaluation of ChatGPT-4V's capabilities in interpret radiological images and formulating treatment plans remains to be explored. Three types of questions were collected: (1) 87 USMLE-style questions, submitting only the question stems and images without providing options to assess ChatGPT's diagnostic capability. For questions involving treatment plan formulations, a five-point Likert scale was used to assess ChatGPT's proposed treatment plan. The 87 questions were then adapted by removing detailed patient history to assess its contribution to diagnosis. The diagnostic performance of ChatGPT-4V was also tested when only medical history was provided. (2) We randomly selected 100 chest radiography from the ChestX-ray8 database to test the ability of ChatGPT-4V to identify abnormal chest radiography. (3) Cases from the 'Diagnose Please' section in the Radiology journal were collected to evaluate the performance of ChatGPT-4V in diagnosing complex cases. Three responses were collected for each question. ChatGPT-4V achieved a diagnostic accuracy of 77.01% for USMLE-style questions. The average score of ChatGPT-4V's treatment plans was 3.97 (Interquartile Range: 3.33-4.67). Removing detailed patient history dropped the diagnostic accuracy to 19.54% (P<0.0001). ChatGPT-4V achieved an AUC of 0.768 (95% CI: 0.684-0.851) in detecting abnormalities in chest radiography, but could not specify the exact disease due to the lack of detailed patient history. For cases from 'Diagnose Please' ChatGPT provided diagnoses consistent with or very similar to the reference answers. ChatGPT-4V demonstrated an impressive ability to combine patient history with radiological images to make diagnoses and directly design treatment plans based on images, suggesting its potential for future application in clinical practice.

Incidence and risk factors for long COVID in children with COVID-19 pneumonia.

There are only a few reports of long COVID including pulmonary function in children after COVID-19 pneumonia. We determined the incidence of long COVID and abnormal pulmonary function in those children and identify risk factors. This cohort study enrolled children admitted with COVID-19 pneumonia during 2021-2022. We gathered clinical characteristics during admission and at follow-up 3 months after. We determined the incidence of long COVID at 39.7% (95% confidence interval [CI]: 30.7%-49.1%). All severe pneumonia cases consistently reported persistent symptoms. Exercise intolerance, cough, and fatigue were the three most common persistent symptoms in 26 (22.4%), 21 (18.1%), and 18 (15.5%) of the patients, respectively. At the follow-up, 21 cases (18.1%) demonstrated persistent abnormal chest radiographs. Three cases (6.9%) demonstrated restrictive ventilatory defects. Among those, one case (2.3%) demonstrated concomitant diffusion defect. Three cases (6.0%) demonstrated exercise-induced hypoxemia after the 6-minute walk test. Comparing spirometry variables between children with long COVID and without revealed significant difference of FEF25-75 (z score) between two groups. Age [adjusted OR (95% CI): 1.13 (1.05-1.22), p value 0.002], allergic diseases [adjusted OR (95% CI): 4.05 (1.36-12.06), p value 0.012], and living in polluted areas [adjusted OR (95% CI): 2.73 (1.18-6.33), p value 0.019] were significantly associated with long COVID. A significant percentage of children developed long COVID after COVID-19 pneumonia. We should give additional attention to those who have exercise intolerance, chronic cough, or fatigue, especially older children, severe cases, children with allergic diseases, and those living in polluted areas.

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance

This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We adopted a deep learning algorithm to concurrently detect the presence of normal findings and 13 different abnormalities in chest radiographs and evaluated its performance in assisting radiologists. Each competing radiologist had to determine the presence or absence of these signs based on the label provided by the AI. The 100 radiographs were randomly divided into two sets for evaluation: one without AI assistance (control group) and one with AI assistance (test group). The accuracy, false-positive rate, false-negative rate, and analysis time of 111 radiologists (29 senior, 32 intermediate, and 50 junior) were evaluated. A radiologist was given an initial score of 14 points for each image read, with 1 point deducted for an incorrect answer and 0 points given for a correct answer. The final score for each doctor was automatically calculated by the backend calculator. We calculated the mean scores of each radiologist in the two groups (the control group and the test group) and calculated the mean scores to evaluate the performance of the radiologists with and without AI assistance. The average score of the 111 radiologists was 597 (587–605) in the control group and 619 (612–626) in the test group (P < 0.001). The time spent by the 111 radiologists on the control and test groups was 3279 (2972–3941) and 1926 (1710–2432) s, respectively (P < 0.001). The performance of the 111 radiologists in the two groups was evaluated by the area under the receiver operating characteristic curve (AUC). The radiologists showed better performance on the test group of radiographs in terms of normal findings, pulmonary fibrosis, heart shadow enlargement, mass, pleural effusion, and pulmonary consolidation recognition, with AUCs of 1.0, 0.950, 0.991, 1.0, 0.993, and 0.982, respectively. The radiologists alone showed better performance in aortic calcification (0.993), calcification (0.933), cavity (0.963), nodule (0.923), pleural thickening (0.957), and rib fracture (0.987) recognition. This competition verified the positive effects of deep learning methods in assisting radiologists in interpreting chest X-rays. AI assistance can help to improve both the efficacy and efficiency of radiologists.

Risk Factors for Severe Respiratory Syncytial Virus Infection in Hospitalized Children.

Respiratory syncytial virus (RSV) is a common cause of bronchiolitis and pneumonia in infants and young children. Starting in December 2010, RSV monoclonal antibody (RSV mAb) was endorsed by Taiwan National Health Insurance and given to children with prematurity and/or congenital heart diseases, which are considered high-risk factors for severe RSV diseases. Investigating other important contributing risk factors is warranted. We conducted a cohort study at National Taiwan University Hospital to determine the rate of severe outcomes among children hospitalized due to RSV infection from 2008 to 2018. Adjusted for age, sex and birth cohorts born before and after RSV mAb endorsement, we identified risk factors for severe RSV infection, defined as the requirement of invasive ventilator support. There were 1985 admissions due to RSV infections. Among them, 66 patients (3.3%) had severe RSV infection. The proportion of severe RSV infections decreased significantly after RSV mAb endorsement. Multivariable analysis revealed that age <1.5 months and cardiovascular and congenital/genetic diseases were high-risk underlying conditions. In addition, bacterial coinfections, elevated creatinine levels and initial abnormal chest radiograph findings posed warning signs for severe RSV infection. Children younger than 1.5 months of age with cardiovascular or congenital/genetic diseases were predisposed to severe RSV infection and might benefit from RSV mAb prophylaxis.

The impact of sputum quality on Xpert positivity in active case-finding for TB.

BACKGROUND: Studies evaluating sputum quality and Xpert® MTB/RIF positivity in the context of active case finding are scarce. We aimed to determine whether sputum quality is associated with Xpert positivity and whether the association differed according to demographic and clinical characteristics.METHODS: A cross-sectional analysis using data from a mass screening programme in Brazilian prisons was conducted from 2017 to 2021. We administered a standardised questionnaire, obtained a chest X-ray and collected a spot sputum sample for Xpert testing. Sputum quality was classified as 'salivary', 'mucoid/mucopurulent' or 'blood-stained'. We used log binomial regressions to estimate the relationship between sputum quality and Xpert positivity, assessing interactions with participant characteristics.RESULTS: Among 4,368 participants for whom sputum quality was assessed, 957 (21.9%) produced salivary specimens, 3,379 (77.4%) had mucoid/mucopurulent sputum and 32 (0.7%) had blood-stained sputum. Xpert positivity was higher among those with mucoid/mucopurulent sputum than among those with salivary samples (12.0% vs. 3.7%). Mucopurulent sputum independently predicted Xpert positivity among individuals with and without symptoms, current smoking and abnormal chest radiographs on CAD4TB.CONCLUSIONS: In our study, sputum appearance independently predicted Xpert positivity, and could be used together with chest X-ray and symptom screening to inform use of Xpert in individual or pooled testing.

OVERVIEW OF CHEST RADIOGRAPH AND CLINICAL SYMPTOMS OF PEDIATRIC PULMONARY TUBERCULOSIS PATIENT IN PROF. DR. I.G.N.G NGOERAH HOSPITAL JANUARY 2021 – JUNE 2022

Tuberculosis (TB) is an infectious disease whose incidence is still quite high in Indonesia. The increasing incidence of pediatric pulmonary TB is still happening today. The diagnosis of pediatric pulmonary TB is still a hot topic because the chest radiograph and clinical symptoms are not typical and are often found in other diseases. The aim of the research is to know the overview of chest radiograph and clinical symptoms of pediatric pulmonary TB patients. This research was conducted at Prof. RSUP. Dr. I.G.N.G Ngoerah January 2021 – June 2022 using a cross-sectional descriptive method and medical record data as source with total sample is 30 people. Univariate analysis was performed using the Statistical Package for the Social Science (SPSS) version 26.0 for MacOS. The results of the study are majority of pediatric pulmonary TB patients at RSUP Prof. Dr. I.G.N.G Ngoerah January 2021 – June 2022 is female (60%), age group 11-16 years (50%), abnormal chest radiograph (96.7%), asymptomatic (63.3%), lymphadenopathy of hilar/paratracheal lymph nodes with/without parenchymal consolidation (96.7%), cough symptoms (30%). Children with pulmonary TB at RSUP Prof. Dr. I.G.N.G Ngoerah January 2021 – June 2022 is dominated by female gender, age group 11-16 years, abnormal chest radiograph, asymptomatic, lymphadenopathy of hilar/paratracheal lymph nodes with/without parenchymal consolidation and symptoms of cough.

Analysis of active surveillance results of pneumoconiosis in Jiangsu Province in 2019

Objective: To conduct active surveillance of pneumoconiosis in Jiangsu Province in 2019, to understand the status quo of occupational health of dust-exposed workers, timely detect occupational health damage and occupational hazard factors, and provide scientific basis for the prevention and control of pneumoconiosis in the future. Methods: From January to December 2019, counties (cities and districts) with relatively concentrated dust hazard enterprises in Jiangsu Province were selected as monitoring points to carry out active surveillance of pneumoconiosis, and occupational health examination of dust workers (3540 people) in 255 enterprises were carried out, including chest radiographs and lung function examinations. SPSS 26.0 statistical software was used for statistical analysis of the data, and Chi-square test was used to analyze the difference level of the rate between different groups such as gender, age and service. Results: Among the 255 employers, small enterprises accounted for the highest proportion (186 enterprises, 72.94%), private economy accounted for the highest proportion (225 enterprises, 88.24%), and machinery manufacturing industry accounted for the highest proportion (44 enterprises, 17.25%). Among the 3540 workers exposed to dust, 6 suspected occupational diseases (0.17%), 50 occupational contraindications (1.41%), and 1469 other diseases or abnormalities (41.50%) were detected. Among the 3540 workers exposed to dust, 399 (11.27%) had abnormal chest radiography results, and 848 (23.95%) had abnormal lung function indicators. The abnormal detection rate of chest radiographs in males (12.25%, 359/2931) was higher than that in females (6.57%, 40/609), and the abnormal detection rate of lung function in females (32.18%, 196/609) was higher than that in males (22.24%, 652/2931), with statistical significance (P<0.05). The age of the workers exposed to dust ranged from 17 to 75 (41.78±10.39) years old, and the years of service in contact with dust of the workers ranged from 0.08 to 42 (6.72±7.47) years. There were statistically significant differences in the detection rate of abnormal chest radiography and abnormal lung function among different age and seniority workers (P<0.05). The abnormal rates of forced vital capacity (FVC), forced expiratory volume in the first second (FEV(1)) and FEV(1)/FVC were 13.79% (488/3540), 15.28% (541/3540) and 1.86% (66/3540), respectively. There were significant differences in the detection rate of abnormal chest radiography and abnormal lung function among workers exposed to different dust types (P<0.05) . Conclusion: There are few suspected occupational diseases detected by active surveillance of pneumoconiosis in Jiangsu Province in 2019, and there are differences in occupational health examination results among workers with different dust types, ages, genders and dust exposure years.

Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms.

Use of computer-aided detection (CAD) software is recommended to improve tuberculosis screening and triage, but threshold determination is challenging if reference testing has not been performed in all individuals. We aimed to determine such thresholds through secondary analysis of the 2019 Lesotho national tuberculosis prevalence survey. Symptom screening and chest radiographs were performed in participants aged ≥15 years; those symptomatic or with abnormal chest radiographs provided samples for Xpert MTB/RIF and culture testing. Chest radiographs were processed using CAD4TB version 7. We used six methodological approaches to deal with participants who did not have bacteriological test results to estimate pulmonary tuberculosis prevalence and assess diagnostic accuracy. Among 17 070 participants, 5214 (31%) had their tuberculosis status determined; 142 had tuberculosis. Prevalence estimates varied between methodological approaches (0.83-2.72%). Using multiple imputation to estimate tuberculosis status for those eligible but not tested, and assuming those not eligible for testing were negative, a CAD4TBv7 threshold of 13 had a sensitivity of 89.7% (95% CI 84.6-94.8) and a specificity of 74.2% (73.6-74.9), close to World Health Organization (WHO) target product profile criteria. Assuming all those not tested were negative produced similar results. This is the first study to evaluate CAD4TB in a community screening context employing a range of approaches to account for unknown tuberculosis status. The assumption that those not tested are negative - regardless of testing eligibility status - was robust. As threshold determination must be context specific, our analytically straightforward approach should be adopted to leverage prevalence surveys for CAD threshold determination in other settings with a comparable proportion of eligible but not tested participants.

COVID-19 in hospitalized infants aged under 3months: multi-center experiences across Turkey.

To investigate coronavirus disease 2019 (COVID-19) in infants aged 0 to 3months because there is currently a significant gap in the literature on the subject. A cross-sectional study was conducted with the involvement of 19 medical centers across Turkey and 570 infants. The majority of the patients were male (58.2%), and the three most common symptoms were fever (78.2%), cough (44.6%), and feeding intolerance (39.9%). The results showed that a small percentage of infants had positive blood (0.9%) or urine cultures (10.2%). Most infants presented with fever (78.2%). Children without underlying conditions (UCs) had mostly a complicated respiratory course and a normal chest radiography. Significant more positive urine culture rates were observed in infants with fever. A higher incidence of respiratory support requirements and abnormal chest findings were seen in infants with chronic conditions. These infants also had a longer hospital stay than those without chronic conditions. Conclusions: Our study discloses the clinical observations and accompanying bacterial infections found in infants aged under 3months with COVID-19. These findings can shed light on COVID-19 in infancy for physicians because there is limited clinical evidence available. What is Known: • COVID-19 in infants and older children has been seen more mildly than in adults. • The most common symptoms of COVID-19 in infants are fever and cough, as in older children and adults.COVID-19 should be one of the differential diagnoses in infants with fever. What is New: • Although most infants under three months had fever, the clinical course was uneventful and respiratory complications were rarely observed in healthy children. • Infants with underlying conditions had more frequent respiratory support and abnormal chest radiography and stayed longer in the hospital.

BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inference

Convolutional neural networks (CNNs) have enabled effective object detection tasks in bioimages. Unfortunately, implementing such an object detection model can be computationally intensive, especially on resource-limited hardware in a laboratory or hospital setting. This study aims to develop a framework called BioEdge that can accelerate object detection using Scaled-YOLOv4 and YOLOv7 by leveraging edge computing for bioimage analysis. BioEdge employs a distributed inference technique with Scaled-YOLOv4 and YOLOv7 to harness the computational resources of both a local computer and an edge server, enabling rapid detection of COVID-19 abnormalities in chest radiographs. By implementing distributed inference techniques, BioEdge addresses privacy concerns that can arise when transmitting biomedical data to an edge server. Additionally, it incorporates a computationally lightweight autoencoder at the split point to reduce data transmission overhead. For evaluation, this study utilizes the COVID-19 dataset provided by the Society for Imaging Informatics in Medicine (SIIM). BioEdge is shown to improve the inference latency of Scaled-YOLOv4 and YOLOv7 by up to 6.28 times with negligible accuracy loss compared to local computer execution in our evaluation setting.

Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification

Pneumonia detection and classification are a vital medical imaging task that purposes to automatically recognize and classify pneumonia-related abnormalities in chest radiographs or Chest X-Ray (CXR) imaging. Accurate and prior identification of pneumonia is important for suitable treatment and recovering patient results. The main drive is to recognize whether an X-ray image indicates the presence of pneumonia or not. A binary classification techniuqe is trained to distinguish normal and pneumonia X-rays. Deep Learning (DL) approaches are revealed major success in automating this process, assisting healthcare specialists in analyzing pneumonia more effectively. This article presents a Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification (WOAFWNN-PDC) technique on CXRs. The purpose of the WOAFWNN-PDC technique is to apply optimal DL approaches for the recognition and classification of pneumonia. In the presented WOAFWNN-PDC technique, Gaussian Filtering (GF) approach is used for the noise removal process. In addition, the MobileNetv3 model is utilized for the feature extraction method. Moreover, the FWNN technique was applied to the classification of pneumonia. Finally, the WOA can be executed for an optimum selection of the parameters related to the FWNN approach. The simulation value of the WOAFWNN-PDC algorithm was assessed on a benchmark medical database. The comparative analysis exhibits better results than the WOAFWNN-PDC technique.