Lung Opacities Research Articles

P11 A Rare and Serious Cause of “Blueberry Muffin Syndrome”

Abstract Urgent dermatology review was sought for a neonate born at term via emergency caesarean section for reduced movements. The pregnancy was complicated by gestational diabetes and infection with suspected coxsackie virus in the second trimester. There were 15 purple/blue nodules consistent with “blueberry muffin syndrome” and associated anaemia (118 g/L). Due to hypoxia, she was given antibiotics for suspected sepsis. TORCH screen was negative and USS abdomen and cranium were normal. A blood film showed B cell aplasia and LDH was raised (710 unit/L). CXR showed a mediastinal mass, left pleural effusion and left lung opacity. A skin biopsy was taken, chest drain placed and the baby transferred to Great Ormond Street. She was subsequently diagnosed with acute myeloid leukaemia following bone marrow and lumbar puncture. All three skin biopsies showed a malignant infiltrate with one displaying positivity for CD12 and CD33. CT and MRI scans have shown widespread nodules in the lungs, liver, kidneys, bones and intra-cranially. She is being treated with chemotherapy which has been complicated by a cardiac arrest and severe mucositis leading to colostomy. She is also NG fed and has sensorineural hearing loss. The nodules seen in “blueberry muffin syndrome” are most commonly extramedullary erythropoiesis but can also represent purpura or metastases. The causes are congenital infections, blood disorders or tumours. There is frequently associated anaemia (as in our case) and hepatosplenomegaly. This case demonstrates a rare and concerning cause of the syndrome and highlights the role dermatologists can play in diagnosis.

Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study.

Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis. Methods: We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses. The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries. Results: The CNN model achieved an average area under the curve (AUC) of 0.961 across all 26 diagnoses in the testing set. COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.8453, [95% CI, 0.8417 to 0.8489]). In external validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.9423 to 0.9702). The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition. Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all P < 0.05). Additionally, our model exhibited no gender bias (P > 0.05). Conclusion: The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation. This research advances medical imaging and offers substantial diagnostic support in clinical settings.

Hermansky-Pudlak Syndrome with an Improvement in the Respiratory Symptoms after the Administration of Pirfenidone: A Case Report.

Herein, we report a case of Hermansky-Pudlak syndrome (HPS) in which respiratory symptoms improved with pirfenidone treatment. A 43-year-old Japanese woman with oculocutaneous albinism presented with a cough and dyspnea. High-resolution computed tomography revealed areas of reticular and frosted lung opacities. The diagnosis of HPS was confirmed by a prolonged bleeding time and HPS1 gene mutation. Generally, there is no effective treatment for interstitial pneumonia associated with HPS except for lung transplantation. In the present case, the cough and dyspnea improved with pirfenidone administration. Therefore, clinicians should administer pirfenidone in challenging transplantation cases and during the waiting period for transplantation.

Correlation between chest computed tomography findings and pulmonary function test results in the post-recovery phase of COVID-19.

The radiological and functional lung sequelae in COVID-19 survivors remain unclear. We compared the chest computed tomography findings of COVID-19 patients with normal and abnormal pulmonary function test results in the post-recovery phase. The data of consecutive patients who underwent pulmonary function tests and chest computed tomography within 14 days after recovery from COVID-19 at two medical centers between May and October 2020 were collected retrospectively. Two thoracic radiologists who were blinded to the clinical information and pulmonary function test results classified the patients according to the computed tomography features, evidence of fibrotic-like changes, and semi-quantitative quantification of the extent of pulmonary abnormalities. The clinical characteristics and computed tomography findings of patients with normal pulmonary function test results were compared with those of patients with abnormal results. A total of 101 COVID-19 survivors, comprising 48 ambulatory and 53 hospitalized patients, were included at a median of 95 days from initial symptom onset. Computed tomography revealed fibrotic-like changes in 10.9% of patients. A reduction in the diffusion capacity of carbon monoxide was the most common lung function abnormality (19.8%). Abnormal diffusion capacity of carbon monoxide was associated with the presence and extension of lung opacities on chest computed tomography scans and fibrotic pulmonary abnormalities. The sensitivity, specificity, and accuracy of reduced diffusion capacity of carbon monoxide for detecting fibrotic-like pulmonary changes on chest computed tomography scans were 72.7%, 87.8%, and 86.1%, respectively. Our study suggests that the presence of an abnormal diffusion capacity of carbon monoxide in the post-recovery phase of COVID-19 is associated with a greater risk of long-term parenchymal lung disease, as evidenced by the presence of fibrotic-like changes on chest computed tomography scans, such as traction bronchiectasis and architectural distortion.

EfficientCovNet: A CNN-based approach to detect various pulmonary diseases including COVID-19 using modified EfficientNet

The ongoing COVID-19 pandemic has brought about severe consequences, including numerous tragic losses of loved ones and significant disruptions to the global economy and daily routines. To curb the spread of the virus, timely and precise detection of COVID-19 cases is crucial. However, the traditional RT-PCR testing system is time-consuming, and its sensitivity can vary depending on the testing environment. In this context, X-ray images of the lungs have emerged as a potential tool for COVID-19 detection, as the virus predominantly affects the lungs. However, distinguishing COVID-19 from other significant pulmonary diseases such as Viral Pneumonia and Lung Opacity is essential to avoid misdiagnosis and ensure appropriate treatment.In this study, we propose a modified efficient Convolutional Neural Network (CNN)-based architecture named EfficientCovNet for accurate classification of different lung diseases, including COVID-19, Viral Pneumonia, and Lung Opacity, as they all share common symptoms of shortness of breath. Our proposed model achieved a remarkable test accuracy of +2% (96.12%) compared to existing CNN models, as demonstrated pictorially in the later sections. Furthermore, we utilized a comparatively large dataset of 20,000 radiograph images encompassing various disease categories, enhancing the robustness and reliability of our model. Our findings highlight the potential of CNN-based models in accurately classifying lung diseases, including COVID-19, and can aid in effective disease management and containment strategies.

A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image

The lungs are two of the most crucial organs in the human body because they are connected to the respiratory and circulatory systems. Lung cancer, COVID-19, pneumonia, and other severe diseases are just a few of the many threats. The patient is subjected to an X-ray examination to evaluate the health of their lungs. A radiologist must interpret the X-ray results. The rapid advancement of technology today can help people in many different ways. One use of deep learning in the health industry is in the detection of diseases, which can decrease the amount of money, time, and energy needed while increasing effectiveness and efficiency. There are other methods that can be used, but in this research, the convolutional neural network (CNN) method is only used with three architectures, namely ResNet-50, ResNet-101, and ResNet-152, to aid radiologists in identifying lung diseases in patients. The 21,885 images that make up the dataset for this study are split into four groups: COVID-19, pneumonia, lung opacity, and normal. The three algorithms have fairly high evaluation scores per the experiment results. F1 scores of 91%, 93%, and 94% are assigned to the ResNet-50, ResNet-101, and ResNet-152 architectures, respectively. Therefore, it is advised to use the ResNet-152 architecture, which has better performance values than the other two designs in this study, to categorize lung diseases experienced by patients.

Acute Respiratory Distress Syndrome due to Monkeypox Virus.

This is the first case of monkeypox virus associated ARDS in a young woman with no medical historyBiological follow-up showed disseminated MPXV and persistent viraemiaTecovirimat was well tolerated.

Classification of COVID-19 and lung opacity using vision transformer on chest x-ray images

There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT’s is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.

Clinico-epidemiological profile of non-survivors of COVID-19 during the last two waves in a tertiary care hospital of North India: A retrospective descriptive study

Background: SARS-CoV-causing COVID-19 resulted in mortality, and the clinic-epidemiological profile at the time of admission of patients who died later could provide an insight into pathophysiological consequences due to infection. Method: Retrospective observational study of 64 RTPCR-confirmed COVID-19 non-survivors was conducted from April - June 2021 and January February 2022. Data were analyzed, and a P value&lt;0.05 was taken as significant. Results: 60.94% and 39.06 % were males and females, and 26.57% &amp; 73.43 % of patients had moderate and severe disease, respectively. Fever, cough, and dyspnea were the most common presenting symptoms. 78.12% and 21.88% had pre-existing (diabetes and hypertension were most common) and no co-morbidities, respectively. 65.62 &amp; 17.19 % of patients had bilateral and unilateral ground glass opacities, respectively. Thrombocytopenia, lymphopenia, neutrophilia, elevated monocytes, and neutrophil-lymphocyte ratio (NLR) of 7.52 were hematological findings. D dimer was elevated. ABG showed low PaO2 and SPO2 %. ALT and AST were elevated. Tachycardia was also present. Compared to the first wave, no significant association of gender with severity was found. However, the percentage of male patients was higher. The association of the duration of stay and co-morbidity with disease severity was significant in both the first and subsequent waves of COVID-19. Conclusion: Co-morbidity, disease severity, and radiological lung opacities play a role in the outcome of COVID-19. The associated findings are hematological, renal, liver, cardiovascular, and arterial blood gas derangements.

Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification.

Quantification of pulmonary opacities using artificial intelligence in chest CT scans during SARS-CoV-2 pandemic: validation and prognostic assessment

PurposeTo assess whether the analysis of pulmonary opacities on chest CT scans by AI-RAD Companion, an artificial intelligence (AI) software, has any prognostic value.BackgroundIn December 2019, a new coronavirus named SARS-CoV-2 emerged in Wuhan, China, causing a global pandemic known as COVID-19. The disease initially presents with flu-like symptoms but can progress to severe respiratory distress, organ failure, and high mortality rates. The overwhelming influx of patients strained Emergency Rooms worldwide. To assist in diagnosing and categorizing pneumonia, AI algorithms using deep learning and convolutional neural networks were introduced. However, there is limited research on how applicable these algorithms are in the Emergency Room setting, and their practicality remains uncertain due to most studies focusing on COVID-19-positive patients only.MethodsOur study has an observational, analytical, and longitudinal design. The sample consisted of patients who visited our emergency room from August 5, 2021, to September 9, 2021, were suspected of having COVID-19 pneumonia, and underwent a chest CT scan. They were categorized into COVID-19 negative and positive groups based on PCR confirmation. Lung opacities were evaluated separately by a team of radiologists and a commercial AI software called AI-Rad Companion (by Siemens Healthineers). After 5 months we gathered clinical data, such as hospital admission, intensive care unit (ICU) admission, death, and hospital stay.ResultsThe final sample included 304 patients (144 females, 160 males) with a mean age of 68 ± 19 std. Among them, 129 tested negative for COVID-19 and 175 tested positive. We used AI-generated opacity quantification, compared to radiologists' reports, to create receiver operating characteristic curves. The area under the curve ranged from 0.8 to 0.9 with a 95% confidence interval. We then adjusted opacity tests to a sensitivity cut-off of 95%. We found a significant association between these opacity tests and hospital admission and ICU admission (Chi-Squared, P < 0.05), as well as between the percentage of lung opacities and length of hospital stay (Spearman's rho 0.53–0.54, P < 0.05) in both groups.ConclusionsDuring the SARS-CoV-2 pandemic, AI-based opacity tests demonstrated an association with certain prognostic markers in patients with suspected COVID-19 pneumonia, regardless of whether a PCR-confirmed coronavirus infection was ultimately detected.

Enhancing Chest X-Ray Image Classification for Lung Diseases through Machine Learning

&#x0D; &#x0D; &#x0D; &#x0D; Chest X-ray imaging is a widely used diagnostic tool for the detection and classification of various lung diseases. In this study, we propose a methodology to enhance the classification accuracy of chest X-ray images by leveraging machine learning techniques. Specifically, we employ the VGG-19 architecture to classify chest X-ray images into five distinct lung conditions: normal, tuberculosis, COVID-19, lung opacity, and pneumonia. A comprehensive dataset consisting of 31,787 chest X-ray images sourced from multiple medical institutions and hospitals worldwide is collected. Each image is labeled by expert radiologists with one of the five lung conditions. The dataset is then preprocessed and trained with 30 epochs. The trained VGGnet model achieved overall test accuracy of 95.11%, demonstrating its capability to accurately classify chest X-ray images into the five targeted lung conditions. The proposed methodology holds significant potential for improving the efficiency and accuracy of lung disease diagnosis based on chest X-ray images. By employing machine learning techniques, we can automate the classification process, providing clinicians with valuable decision support and expediting treatment plans. Moreover, the developed model can assist in the identification of lung diseases, including critical conditions such as COVID-19, enabling prompt and effective patient management.&#x0D; &#x0D; &#x0D; &#x0D;

Long-term Findings in Bullous Sarcoidosis: A Case Report and Literature Review.

Pulmonary sarcoidosis may occasionally present with large bullae, but the clinical implications of this finding remain unclear. We herein report the complete clinical course of a case of pulmonary bullous sarcoidosis. Chest computed tomography initially showed subpleural and peribronchovascular lung opacities, and bullae spontaneously developed in adjacent less-affected regions, probably via a retraction mechanism. Bullae progression was refractory to corticosteroid treatment and associated with deterioration of respiratory symptoms. The later phase involved repeated bacterial and fungal infections of the bullous lungs, eventually causing respiratory failure and mortality. Postmortem examinations revealed aggressive pulmonary Mycobacterium avium infection and diffuse alveolar damage.

Anomalies Detection in Chest X-Rays Images Using Faster R-CNN and YOLO

Lungs are crucial parts of the human body and can be captured as Chest x-ray images for disease diagnosis. Unfortunately, in many countries, hospitals and healthcare centers lack qualified doctors for medical images-based diagnosis. Recent numerous advancements in artificial intelligence have deployed with many medical applications to support doctors for disease diagnosis. In our research, we have leveraged YOLOv5s to identify and extract lungs and performed segmentation tasks with Fast R-CNN and YOLOv5 for comparison. The lung region abnormality detection models have pretty good average precision. For example, the YOLOv5 model outperforms both in terms of training time, prediction, and accuracy, with the AP@.5 and AP@.5:.95 metric values, 0.616 and 0.322 on 2,500 images of 5 abnormalities (aortic enlargement, cardiomegaly, lung opacity, pleural effusion, and pulmonary fibrosis).

Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.

Computed tomography (CT) scans, or radiographic images, were used to aid in the early diagnosis of patients and detect normal and abnormal lung function in the human chest. However, the diagnosis of lungs infected with coronavirus disease 2019 (COVID-19) was made more accurately from CT scan data than from a swab test. This study uses human chest radiography pictures to identify and categorize normal lungs, lung opacities, COVID-19-infected lungs, and viral pneumonia (often called pneumonia). In the past, several CAD systems using image processing, ML/DL, and other forms of machine learning have been developed. However, those CAD systems did not provide a general solution, required huge hyper-parameters, and were computationally inefficient to process huge datasets. Moreover, the DL models required high computational complexity, which requires a huge memory cost, and the complexity of the experimental materials' backgrounds, which makes it difficult to train an efficient model. To address these issues, we developed the Inception module, which was improved to recognize and detect four classes of Chest X-ray in this research by substituting the original convolutions with an architecture based on modified-Xception (m-Xception). In addition, the model incorporates depth-separable convolution layers within the convolution layer, interlinked by linear residuals. The model's training utilized a two-stage transfer learning process to produce an effective model. Finally, we used the XgBoost classifier to recognize multiple classes of chest X-rays. To evaluate the m-Xception model, the 1095 dataset was converted using a data augmentation technique into 48,000 X-ray images, including 12,000 normal, 12,000 pneumonia, 12,000 COVID-19 images, and 12,000 lung opacity images. To balance these classes, we used a data augmentation technique. Using public datasets with three distinct train-test divisions (80-20%, 70-30%, and 60-40%) to evaluate our work, we attained an average of 96.5% accuracy, 96% F1 score, 96% recall, and 96% precision. A comparative analysis demonstrates that the m-Xception method outperforms comparable existing methods. The results of the experiments indicate that the proposed approach is intended to assist radiologists in better diagnosing different lung diseases.

Case Report: Pneumonia Like Mass with Spontaneous Resolution

Background: Pneumonia can easily mimic malignancies. Pulmonary nodule finding raise concern for lung cancer. A single lung opacity less than 3 centimetres in diameter is referred to as a solitary pulmonary nodule (SPN). These nodules sometimes incidentally discovered during routine computed tomography (CT) scan of the chest in relatively asymptomatic patients. We present unusual case of pneumonia like mass with spontaneous resolution.Case: Woman, 38-year-old, with solitary pulmonary nodule which was found accidentally in the left lung upper lobe, without any respiratory symptoms in June 2021. In August 2021, a chest CT scan was done for evaluating and the solitary nodule of the left lung upper lobe had resolved spontaneously without any treatment. The patient was prepared for diagnostic bronchoscopy at Persahabatan Hospital with the findings of normal bronchi and the branches. A bronchial washing was performed on the left B1+2 segment and cytology, fungal, microorganism and molecular tuberculosis examination were performed.Discussion: Pulmonary solitary nodule in left lung upper lobe in the beginning and spontaneous resolution after 2 months evaluation without any specific treatment, consider period of pneumonia. Whenever a patient is found to have an SPN, it is essential to determine the patient’s risk for malignancy. It is important to consider any pulmonary nodule to be malignant or not, and how it presents on CT Imaging.Conclusion: Pulmonary nodule may be found during pneumonia and may resolve spontaneously. The possibility of malignancy must still be considered. Awareness of condition from the history is important to help reassure patient about the disease even before diagnostic procedure was made.

Acetylcysteine and budesonide for the treatment of refractory Mycoplasma pneumoniae pneumonia in children: a clinical observation

BackgroundTo examine the clinical impact of bronchoscope alveolar lavage (BAL) combination with budesonide, ambroxol + budesonide, or acetylcysteine + budesonide in the treatment of refractory Mycoplasma pneumoniae pneumonia (RMPP).MethodsEighty-two RMPP patients admitted to Pediatrics at The First People’s Hospital of Zhengzhou were retrospectively evaluated between August 2016 and August 2019. All patients were administered BAL in addition to intravenous Azithromycin, expectoration, and nebulizer inhalation. The medications added to the BLA separated the patients into the Budesonide group, Ambroxol + budesonide group, and acetylcysteine + budesonide group. Analyzed were the variations in laboratory examination indices, improvement in lung imaging, overall effective rate, and adverse responses in the three groups.ResultsThe laboratory test indices of patients in all three groups improved significantly relative to pre-treatment levels, and the results were statistically significant. After therapy, there were no significant differences between the three groups in terms of white blood cell (WBC), C-reactive protein (CRP), or erythrocyte sedimentation rate (ESR). Serum lactate dehydrogenase (LDH) and serum ferritin (SF) varied significantly across the three groups (P < 0.05). In the acetylcysteine + budesonide group, the absorption rate of lung imaging lesions and clinical efficacy were superior to those of the other two groups. There were no significant differences between the three groups in the occurrence of adverse events (P > 0.05).ConclusionsBLA-coupled acetylcysteine + budesonide was superior to the other two groups in enhancing the effectiveness of RMPP in children, which might increase lung opacity absorption and minimize lung inflammation.

Lung Opacity And Coronary Artery Calcium Score: A Combined Tool For Risk Stratification And Outcome Prediction In Covid-19 Patients

Lung Opacity And Coronary Artery Calcium Score: A Combined Tool For Risk Stratification And Outcome Prediction In Covid-19 Patients

Postmortem Diagnostic Imaging to Evaluate Idiopathic Hypertrophic Cardiomyopathy in a Roborovski Hamster

Idiopathic cardiomyopathy in hamsters can cause death due to cardiac failure. The current case study investigated the capability of imaging to reveal possible cardiomyopathy in a dead hamster. To this end, the cadaver of a 6-month-old male Roborovski dwarf hamster, which showed acute respiratory symptoms a few days before its death, was examined by virtopsy to discover the cause of death. Postmortem radiography was not efficient enough to evaluate the heart due to postmortem lung atelectasis that increases lung opacity and diminishes the contrast between lung and heart. Postmortem computed tomography can be helpful for the assessment of cardiac size. Consistent with postmortem echocardiographic studies, an increased thickness of the left ventricular parietal wall and the interventricular septum and dilation of the left atrium were observed. Thus, hypertrophic cardiomyopathy was determined by imaging and confirmed by the conventional necropsy approach. It showed that the cause of death was acute cardiac failure following idiopathic hypertrophic cardiomyopathy.

Diesel siphoning induced chemical pneumonitis: a case report.

In this case study, a 16-year-old boy gave a history of diesel fuel siphoning from a motor vehicle tank, which brought him to our emergency room. He complained of coughing, breathing difficulties, and chest discomfort upon admission to the hospital. Patchy bilateral parenchymal lung opacities consistent with acute chemical pneumonitis were seen in radiological imaging tests. Treatment included supportive care, oxygen supplementation, and intravenous antibiotics. The patient's symptoms improved gradually throughout his hospitalization, and he was eventually discharged home with a good prognosis. Siphoning is a common practice in developing countries like Bangladesh. Workers at automobile transfer hydrocarbon products from one vehicle to other. However, its aspiration can cause a pneumonia like features and may wrongly misdiagnose. Diagnosis is made mainly on history taking. Physicians ought to know that patients exposed to diesel fuel may develop chemical pneumonitis, and they should consider this for an early diagnosis and effective treatment that can lead to favourable outcomes.