Computed Tomography Scan Imaging Research Articles

High-Precision Detection of Lung Adenocarcinoma Using Augmented VGG16 and Transfer Learning

Objectives: The research aims to enhance the effectiveness of the deep learning model for detecting lung adenocarcinoma using VGG16 CNN with transfer learning on medical images. The proposed model addresses the overfitting issues of existing models, thereby improving lung cancer detection accuracy. Methods: The lung CT scan images are analyzed for detection and classification using VGG16, leveraging transfer learning techniques. The dataset includes labeled lung images from LUNA 16, Kaggle, and other datasets, which are augmented to introduce variability and reduce overfitting. The images are resized format to 224 × 224 pixels. During the validation and training phases, the model's performance is evaluated based on accuracy, and precision. Findings: The adjusted VGG16 model attains a training accuracy of 99.42%, a validation accuracy of 99.13%, and a validation precision of 95.45%, indicating strong generalization capabilities, outperforming other existing models. Novelty: This research presents a new method for detecting lung cancer by combining advanced preprocessing and data augmentation techniques with the VGG16 architecture using transfer learning. This combination greatly enhances the model's ability to detect cancer accurately, setting a new standard in the field. Keywords: Lung cancer, VGG16, Transfer learning, Data augmentation, medical imaging, CT (Computer Tomography)

An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images

A stroke is a potentially fatal brain attack that causes an interruption in the blood supply to the brain. As a result, brain cells start to die due to a lack of oxygen and nutrients. After a stroke, every minute is critical. A million or more brain cells perish every minute during a stroke. The prompt identification of a stroke can prevent lasting brain damage or even save the patient’s life. Doctors advise computed tomography (CT) images of the brain for earlier stroke detection. If doctors delay CT diagnosis or may make erroneous diagnoses, this can be life-threatening. For that reason, an automatic diagnosis of stroke from a brain CT scan image will be beneficial for stroke patients. This study moderates three pre-trained convolutional neural network (CNN) models named Inceptionv3, MobileNetv2, and Xception by updating the top layer of those models using the transfer-learning technique based on CT images of the brain. A new ensemble convolutional neural network (ENSNET) model is proposed for automatic brain stroke prediction from brain CT scan images. ENSNET is the average of two improved CNN models named InceptionV3 and Xception. We have relied on the following metrics: accuracy, precision, recall, f1-score, confusion matrix, accuracy versus epoch, loss versus epoch, and the receiver operating characteristic (ROC) curve to assess performance matrices. The accuracy of the moderated Inceptionv3 is 97.48%, the moderated MobileNetv2 is 83.29%, and the moderated Xception is 96.11%. Nonetheless, the suggested ensemble model ENSNET performs better than the other models when it comes to the diagnosis of stroke from brain CT scans, providing 98.86% accuracy, 97.71% precision, 98.46% recall, 98.08% f1-score, and 98.74% area under the ROC curve(AUC). Therefore, the proposed model ENSNET can detect strokes from computed tomography images of the brain more successfully than other models.

Kidney Disease Classification

Kidney diseases, such as tumors, cysts, and stones, are serious health conditions that affect millions globally. Medical imaging, particularly Computed-Tomography (CT) scans, plays an important role in diagnosing these conditions. Although, manual analysis of CT images by radiologist can be tiresome, fallible, and affected by human variability. This paper introduces a deep learning approach that employs Convolutional Neural Networks (CNN) for the automated classification of kidney CT scan images into four distinct categories : normal, cysts, tumors, and stones. The system is designed to boost diagnostic accuracy, reduce human error, and expedite clinical decision making. Through data preprocessing, CNN architecture design, training, and evaluation, the proposed model achieved a classification accuracy of 92%. This study highlights the significance of CNNs in medical image analysis anddtheir implicit for real- time deployment in clinical surroundings. Additionally, we compare our CNN-based approach with other advanced architectures like Vision Transformers, demonstrating CNNs' robustness and efficiency in this domain.

Coronary atherosclerotic plaque burden and characterization by spectral computed tomography scan in patients with high lipoprotein a and non-hemodynamically significant stenosis

Abstract Introduction Atherosclerotic coronary disease increases morbidity and mortality worldwide. Lipoprotein A (LpA) has emerged as a cardiovascular risk marker, but little is known of its direct influence in plaque volume and composition. Spectral computed tomography (CT) technology allows to characterize coronary plaques with new promising tools. Purpose To evaluate the association of LpA with the extent and composition of non-significant coronary plaques producing stenosis <50%. Methods We prospectively recruited 109 patients referred for noninvasive CT angiography (November 2023-January 2024) with suspected coronary artery disease. 20 had totally/partially non-calcified, non-significant coronary plaques. Plaques were segmented and volume was measured. Their composition was stablished as calcified (>200 UH), lipidic (<60 UH) and fibrous (60-200 UH). Spectral data-based plaque parameters were analyzed, including radiological attenuation on polyenergetic images (CTPI), on virtual low/high monoenergetic images (CT40KeV and CT140KeV, respectively), variation in attenuation across monoenergetic levels (slope40-140 KeV) and Z-effective value. CT scan images were acquired with a 128 detectors dual-layer spectral scan with prospective-retrospective/synchronization. Intravascular iodine-based contrast was administered. LpA was stablished as high (≥25 mg/dL) or low (<25 mg/dL). Results Mean age was 60 ± 11. Cardiovascular risk factor were equally distributed between groups. We analyzed 33 plaques. Total plaque volume was significantly higher in high LpA patients (125.9 mm3 vs 62.1 mm3). There were no differences in plaque composition by LpA (Table). Additionally, spectral data showed significantly higher plaque attenuation on CT140KeV images in high LpA patients (36.07 vs 21.32 HU; p = 0.05). There were no differences for the rest of spectral parameters. Conclusions Plaque volume was higher in patients with non-significant atherosclerotic disease and high LpA, but these plaques showed similar composition in high and low LpA patients. These patients might, therefore, respond to the same conventional treatment event though there is not specific treatment for high LpA yet. Additionally, spectral CT140KeV images could add more precision differentiating these plaques since radiological attenuation in these reconstructions is higher in patients with high LpA.TableFigure

Acute irreducible anterior shoulder dislocation due to interposition of the subscapularis muscle and the lesser tuberosity: a case report.

Efforts to reduce an anterior shoulder dislocation can fail due to numerous mechanical obstructions caused by soft tissue interposition (long head of the biceps, rotator cuff muscles, labrum, musculocutaneous nerve) and/or bony elements (displaced fragment of a greater tuberosity or glenoid fracture, bone impaction such as a Hill-Sachs lesion fixed on the glenoid rim, a bony Bankart lesion). Herein, we report the case of a 35-year-old man who sustained an anterior shoulder fracture-dislocation of his left shoulder after a fall. Despite a postreduction radiological examination that appeared misleadingly reassuring, subtle signs of persistent subluxation raised concerns. A computed tomography (CT) scan revealed subscapularis muscle entrapment along with avulsion of its bony insertion from the lesser tuberosity of the humerus, and a comminuted avulsion fracture of the greater tuberosity of the humerus. The patient underwent surgery using a deltopectoral approach. This involved releasing the entrapped subscapularis muscle and fixing the two fractured fragments. The lesser tuberosity was reduced and secured with two cannulated screws, and the comminuted fragment of the greater tuberosity was reattached using transosseous sutures. At 12-month follow-up, the patient achieved a Constant-Murley score of 85 of 100, with limitation in internal rotation at L3 but no signs of instability or new dislocation episode. This case underscores the importance of confirming shoulder reduction on at least two orthogonal views and paying close attention to the patient's feedback about sensation in their shoulder. Additionally, it highlights the utility of CT or magnetic resonance imaging scans if doubt exists about the integrity of the reduction.

Diagnostic Accuracy of Computed Tomography and Clinical Examination in the Assessment of Mandibular Invasion of Oral Squamous Cell Carcinomas

Introduction: Computed tomography (CT) scan precisely shows soft and hard tissues in the same test hereby determines the lesion extension, involvement of regional node as well as of bone. Current study was aimed to evaluate the efficacy of clinical examination and CT to assess mandibular invasion in oral squamous cell carcinoma. Materials and methods: This cross-sectional study was conducted at Dhaka Dental College Hospital from July, 2016 to July, 2017 among conveniently selected 35 patients of histologically confirmed squamous cell carcinoma which was close to the mandible. The patients underwent proper clinical examination. CT scan was performed; preoperative staging and treatment plan was formulated according to the status of bone invasion. After mandibulectomy, the resected specimens were sent for histopathology. The findings from clinical examination and CT were then correlated with the gold standard, postoperative histopathology. Results: Clinical examination accurately detected 22 cases to have bone invasion and 8 cases with no bone invasion. It also gave 2 false positive and 3 false negative results. On the other hand CT accurately detected 24 cases to have bone invasion and 9 cases with no bone invasion. It provided one false positive and one false negative result. However, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of clinical examination were 88%, 80%, 91.67%, 72.73%, 85.71% respectively (p = 0.0002) and of CT were 96%, 90%, 96%, 90%, 94.28% respectively (p < 0.0001). Clinical examination and CT were found sensitive enough and have an acceptable range of specificity as primary investigative modalities. CT scan outperforms clinical examination in terms of sensitivity, specificity, NPV, and accuracy. Conclusion: CT scan imaging is the method of choice for planning treatment in advanced oral squamous cell carcinoma. This study reveals how a CT scan can provide additional diagnostic value to detect bone invasion. Update Dent. Coll. j: 2024; 14(2): 21-26

Implementation of the 300-mL Rule for the Management of Traumatic Hemothorax.

Traumatic hemothorax (HTX) is often managed with tube thoracostomy (TT); however, TT carries a high complication rate. In 2017, a guideline was implemented at our Level I trauma center to observe traumatic HTX ≤300mL in hemodynamically stable patients. We hypothesized that this guideline would decrease TT placement without increasing observation failure rates. This was a single-center retrospective review of all adult patients admitted with a HTX on computed tomography (CT) before (2015-2016) and after (2018-2019) the guideline implementation. Exclusion criteria were TT placement prior to CT scan, absence of CT scan, death within 5 days of admission, and a concurrent pneumothorax (PTX) >20mm. HTX volume was calculated using CT scan images and Mergo's formula: V=d 2xL (V: volume; d: depth; L: length). The primary outcome was observation failure, defined as the need for TT, video-assisted thoracoscopic surgery, thoracotomy after repeat imaging or worsening of symptoms and pulmonary morbidity. A total of 357 patients met inclusion criteria, of whom 210 were admitted after guideline implementation. There were no significant differences in baseline demographics, comorbidities, or injury characteristics across both cohorts. The post-implementation cohort had a significant increase in observation rate (75% vs 59%) and a decrease in TT placement (42% vs 57%). Moreover, the post-implementation group had a statistically significant shorter hospital (6 vs 8 days) and ICU (2 vs 3 days) LOS. No significant differences in observation failure, pulmonary complications, 30-day readmission, or 30-day mortality were observed across both cohorts. The implementation of the 300mL guideline led to a decrease in TT placement without increasing observation failure or complication rates. Level III, Therapeutic/Care Management.

Heterotopic High-Grade Salivary duct Carcinoma Likely Arising within Cervical Lymph Nodes

Abstract Introduction/Objective Salivary duct carcinoma arising in heterotopic salivary gland tissue is an extremely rare occurrence, with less than five cases reported in the literature. We present a unique case of heterotopic high-grade salivary duct carcinoma likely arising within cervical lymph nodes. Methods/Case Report A 56-year-old male presented with enlarged cervical lymph nodes for few months. Physical examination was unremarkable. Computerized tomography (CT) scan of head and neck showed two enlarged level 1B lymph nodes, measuring 1.0 and 1.4 cm, with unremarkable salivary glands. Patient had left cervical level II lymph node biopsy that showed metastatic poorly differentiated carcinoma. Immunohistochemically, the tumor cells were positive for GATA3, cytokeratin 903, androgen receptor, p16 (variable nonspecific), and were negative for TTF-1, p40, p63, ER, PR and NKX3.1, favoring salivary gland ductal carcinoma. Positron emission tomography (PET) CT scan showed a hypermetabolic left submandibular gland and level 2A lymph nodes. Subsequently, the patient underwent left modified radical neck dissection. Sectioning of the submandibular gland revealed no discrete lesions. Multiple, firm to rubbery lymph nodes ranging from 0.4 cm to 2.3 cm, with tan-pink cut surfaces were identified. Microscopic examination showed normal submandibular salivary gland parenchyma. However, eight out of nineteen lymph nodes were positive for metastatic high-grade salivary duct carcinoma comprising tumor cells forming nests and cords with ample eosinophilic cytoplasm, pleomorphic round to oval nuclei and numerous mitotic figures. Several benign and malignant lymph nodes in levels I, II and IV showed heterotopic salivary gland tissue. Immunohistochemical stains for androgen receptor and p40 were positive and negative respectively, supported the final diagnosis of high-grade salivary duct carcinoma. Results (if a Case Study enter NA) NA Conclusion Given the absence of carcinoma in the completely sampled submandibular gland, the origin of the intra- nodal tumor deposits was most likely to be heterotopic salivary gland tissue. This case emphasizes the importance of meticulous pathological sampling required for definitive diagnosis.

Experimental investigation of failure mechanism of fissure-filled sandstone under hydro-mechanical conditions

Fissure fillings are critical to the hydro-mechanical properties of jointed rock masses in rock engineering. In this study, triaxial seepage tests were performed on standard cylindrical fissure-filled sandstone. The characteristics of stress–strain relationships, absorption and consumption of energy, variations in deformation resistance, and permeability evolution during the experimental process, along with the crack development observed in post-failure computed tomography scan images of the sandstone specimens were analyzed. The results demonstrate that the fillings improve the energy capacity and reduce the damage accumulation of sandstone specimens, with sand-filled specimens performing better than mud-filled specimens, especially at lower bridge angles. The fillings can reduce the depth of crack extension and lessen the influence of prefabricated fissures on sandstone failure, with this effect diminishing as the rock bridge angle increases. Permeability decreases in the pre-peak failure stage as the fillings improve the deformation resistance of the sandstone specimens. In the post-peak failure stage, the fillings and rock debris generated by the sandstone failure move within the developed fractures, causing significant fluctuations in permeability. These findings deepen the understanding of the hydro-mechanical properties of jointed rocks and provide a scientific basis for stability analysis in rock engineering.

Dynamics of localized accelerated corrosion in reinforced concrete: Voids, corrosion products, and crack formation

A comprehensive 3D imaging analysis was conducted to investigate the corrosion process in reinforced concrete (RC) samples at various stages of the accelerated galvanostatic corrosion process. A state-of-the-art X-ray computed tomography (CT) scan technology was utilized to collect high-resolution 3D images of the specimen. The CT imaging was complemented and validated using a multi-faceted approach utilizing Scanning electron microscopy (SEM) and Raman spectrometry techniques. The distribution and evolution of voids, corrosion products, and cracks were investigated. A localized corrosion was observed that has some similarity to pitted corrosion but not as wide spread along the bar. The micro-scale imaging investigations revealed a gradual increase in the diameter of voids as the time of corrosion increased, especially after 8 days of accelerated corrosion process at which a significant decrease in small-sized voids (≤0.11 mm) accompanied by an increase in other void sizes was observed. At 10 and 12 days of accelerated corrosion process, the volume of larger-sized voids (≥0.35 mm) continued to increase, indicating a rapid corrosion progress and the formation of corrosion-induced cracks. In addition, a thorough X-ray CT analysis allowed us to differentiate between various categories of corrosion products. Notably, iron oxides and iron hydroxides were found to dominate the corrosion products. Understanding the composition and distribution of these corrosion products is essential as they play a significant role in the degradation and deterioration of the concrete structure. The coexistence of iron oxides and iron hydroxides in the region of corrosion products were confirmed using Raman spectroscopy analysis. The CT scan images captured the evolution of cracks at different time intervals, revealing a strong correlation between the initiation of cracks and the presence of corrosion products. Subsequently, during the cracking in the matrix, while the propagation of corrosion products within the matrix contributed to the expansion of cracks. SEM images and elemental analysis confirmed that these cracks were filled with corrosion products.

Classification of Lung Cancer using Pre-Trained Deep Learning Models

Many Computer Assisted Diagnosis (CAD) systems have been designed and used in recent past for diagnosingdifferenttypes of cancer. Identification of carcinoma at an earlier stage is more important, and it is made possible due to the use modern image processing and deep learning methods.The occurrence of Lung cancer is seen to be increased and Computed Tomography (CT) scan images were utilized in investigation to locate and classify lung cancer, also for determining the severity of those cancer. This study is aimed at employing pre-trained deep neural networks for classification of lung cancer images. A gaussian-based approach is used to segment CT scan images. In this research exploits a transfer learning-based classification method for the chest CT images acquired from Cancer Image Archive and available in the Kaggle platform. Pre- trained models VGG and RESNET were trained using segmented chest CT images, and their performance was evaluated using different optimization algorithms were analyzed. Keywords: Computer Aided Diagnosis, lung cancer, deep learning, CT image, gaussian, transfer learning, pre-trained models, optimization algorithms.

Risk factors of hematoma after SWL for renal calculi: analysis from RCTs and a literature review.

To identify risk factors of perinephric hematoma following extracorporeal shockwave lithotripsy (SWL) for renal calculi through combined analysis of two randomized controlled trials. This post-hoc analysis included adult patients with solitary renal calculi ranging from 5 to 15mm, treated with SWL between 2016 and 2022. All patients received cross-sectional imaging (either non-contrast computer tomography scan or magnetic resonance imaging) two days post-SWL to assess the presence and severity of perinephric hematoma. Among 573 patients analyzed, 173 (30.9%) developed perinephric hematoma by Day 2 post-SWL. Multivariate logistic regression identified higher total energy delivered (odds ratio [OR] = 1.533, p = 0.003), higher mean stone density (OR = 2.603, p = 0.01), higher maximal stone density (OR = 3.578, p = 0.03), and lower pole stone location (OR = 1.545, p = 0.029) were risk factors for the development of hematoma. Conversely, the stepwise ramping protocol was a protective factor for hematoma formation. (OR = 0.572, p = 0.042). This study elucidates key factors influencing the risk of perinephric hematoma post-SWL, highlighting the importance of procedural adjustments such as the stepwise ramping protocol to reduce complications. These insights call for targeted patient and treatment strategy optimization to enhance SWL safety and efficacy.

Serial Case of CT Scan Imaging in Fracture of Midface Region

Background: Fractures in the face region are frequently seen in motor vehicle accidents, as the face is highly exposed during such incidents. Motor vehicle accidents are common in Bali, with fracture of the face region is the 2nd most common injury found in radiologic examination. Computed tomography (CT) scans are crucial for assessing these fractures, allowing for an accurate evaluation of severity to plan the patient treatment. Case: We present serial cases CT scan images of complex midface fractures such as Le Fort, tripod, NOE and orbital wall fracture and its characteristic imaging on CT-imaging. Conclusions: CT scan may guide treatment plans aimed at minimizing disability and enhancing the patient's quality of life. Understanding the imaging characteristics of midface fractures is thus essential for effective diagnosis and management.

Investigation of aggregate gradation on air voids distribution in porous asphalt concrete using X-ray CT scanning images

Porous asphalt concrete (PAC) is typically used as a paving material for porous asphalt pavement in rainy areas due to its abundant interconnected pores structure. Not all interconnected pores in PAC are valid for permeability, mainly influenced by the composition of aggregate gradation. The main purpose of this paper is to investigate the effect of aggregate gradation on pores structure distribution in PAC by X-ray computed tomography (CT) system and digital image processing (DIP) techniques. The distributions of porosity, number and average equivalent diameter of air voids in middle part are much more evenly compared to the both ends in PAC specimens, accounting for about 70 % of the total of specimen height. The average equivalent diameter of interconnected air voids mainly ranges from 0.5 mm to 5 mm in PAC, accounting for about 80 % of the total numbers. For PAC with similar porosity, the larger the normal maximum aggregate size (NMAS), the less the number and the larger the average equivalent diameter of air voids (interconnected air voids), the smaller the difference between the interconnected porosity and porosity. For PAC with same NMAS, as the porosity increase, the number and average equivalent diameter of air voids (interconnected air voids) are respectively decreased and increased gradually, the difference between the interconnected porosity and porosity is also showed a decreasing trend. And then, the correlated equation is established among porosity, interconnected porosity and the composition of aggregate gradation. The distribution of air voids (interconnected air voids) size could be well described by the two-parameter Weibull model, and the relationships are explored and developed between Weibull model parameters and the composition of aggregate gradation.

Variants in the superior attachment of uncinated process and their corresponding frontal sinus drainage pattern: a computed tomography-based retrospective clinical study

Background: Understanding the morphology and spatial relationships of the uncinate process and its associated spaces is crucial for diagnosing and treating conditions affecting sinus drainage and ventilation. This consistent anatomical structure is an important surgical landmark for endoscopic sinus surgery. Methods: Computed tomography (CT) records of 100 patients over two years were extracted via the hospital's PACS system. The type of superior insertion of uncinate process (UP) was observed in coronal cuts of CT scan images, and classification was done based on the Friedmann and Landsberg classification system and tabulated. The corresponding variations in the frontal sinus outflow tract were noticed, and their effect on frontal sinusitis was recorded. Sinusitis of the frontal sinus was in guidance with EPOS 2020 (mucosal changes within the ostiomeatal complex or sinus). Results: Type 2 was the most common of uncinate attachments (39.5%). The least common variant was type 5 (4%). Medial-type frontal sinus drainage pathway was observed in 78.5% of patients. Sinusitis was present in 23.5% of our study population. Type 2 had a 25% contribution to the occurrence of sinusitis. The medial frontal sinus drainage pathway contributes 24% to sinusitis with a significant p value of 0.010. Conclusions: Uncinate process, with its varying superior attachment, influences the frontal sinus outflow tract and may contribute to the development of frontal sinusitis. Awareness of the possible variations in the attachment of the uncinate process is vital in the preoperative planning process, which will serve as a road map to surgeons and aid in preparedness for intraoperative complications.

On Mapping Local Foam Mobility in Porous Media from Computed Tomography Data

Summary Recovering apparent viscosity and foam texture fields from coreflooding experiments is challenging, even with modern computed tomography (CT) scan equipment. In this work, we present an explicit expression for efficiently calculating effective foam viscosity and propose an improved procedure for processing CT scan images to obtain accurate water saturation profiles. Using these techniques, we processed data from a CT scan of a coreflooding experiment, showing that the increase in effective foam viscosity due to foam generation occurs early during injection and before breakthrough. The fast increment in apparent viscosity is due to foam generation before breakthrough. After breakthrough, foam texture reaches its maximum, and effective foam viscosity grows logarithmically over time as the foamed gas sweeps out the water phase. The pressure drop obtained by using the effective foam viscosity showed good agreement with the experimentally obtained values before breakthrough. The workflow proposed here could be readily adapted to other foam models, provided reasonable estimates for these new quantities can be determined from experiments.

Optimizing pulmonary carcinoma detection through image segmentation using evolutionary algorithms

<span lang="EN-US">This paper’s goal is to suggest an image segmentation technique for use with medical images, specifically computer tomography scan images, to aid doctors in understanding the images. To address a variety of picture segmentation issues, it is necessary to investigate and apply novel evolutionary algorithms. The study focuses on pulmonary carcinoma, which is the cancer that affects males the most frequently across the globe. For proper treatment and life-saving measures, early identification of lung cancer is essential. To identify lung cancer, doctors frequently employ the computed tomography imaging technique. In order to extract tumours from lung scans, the study analyses the effectiveness of three optimization algorithms: k-means clustering, particle swarm optimization, and modified guaranteed convergence particle swarm optimization. The study also examines the pre-processing performance of four filters, namely the mean, bilateral, gaussian, and laplacian filters, shows that the bilateral filter is best suited for CT scans of the body. To test the proposed technique on 30 examples of lung scans. The proposed algorithm is tested on 30 sample lung images. The results show that the modified guaranteed convergence particle swarm optimization algorithm has the highest accuracy of 96.01%.</span>

Enhancing medical image classification via federated learning and pre-trained model

The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. However, the potential privacy concerns that arise from sharing confidential patient information to train traditional centralized machine learning models cannot be ignored. Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the pre-trained models in the FL environment for medical image classification. The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), along with the EfficientNet model, are being used as the local models. The trainable parameters from the local models are fed as input for building the global model. Pre-trained models trained on extensive datasets, possess valuable characteristics that can be utilized by FL models trained on proprietary datasets. Implementing this method can improve the efficacy and precision of FL models while also ensuring data confidentiality. The proposed model is evaluated using two distinct medical imaging datasets: Magnetic Resonance Image(MRI) and Computed Tomography (CT) scan images. The research highlights the advantages of utilizing pre-trained models in federated learning for medical image classification (MIC). The model’s performance is assessed acrossseveral assessment criteria, demonstrating the model exhibited a satisfactory accuracy rate of 97.4% and 98.8% for MRI and CT scan images, respectively. The model is evaluated concerning to Diagnostic Odds Ratio (DOR), where the proposed global model has exhibited 1164.54 for the MRI images and 6825.17 for CT scan images, and the values have outperformed the pretrained model.

Biomechanical changes of oblique lumbar interbody fusion with different fixation techniques in degenerative spondylolisthesis lumbar spine: a finite element analysis

ObjectiveThere is a dearth of comprehensive research on the stability of the spinal biomechanical structure when combining Oblique Lumbar Interbody Fusion (OLIF) with internal fixation methods. Hence, we have devised this experiment to meticulously examine and analyze the biomechanical changes that arise from combining OLIF surgery with different internal fixation techniques in patients diagnosed with degenerative lumbar spondylolisthesis.MethodsSeven validated finite element models were reconstructed based on computed tomography scan images of the L3-L5 segment. These models included the intact model, a stand-alone (S-A) OLIF model, a lateral screw rod (LSR) OLIF model, a bilateral pedicle screw (BPS) OLIF model, an unilateral pedicle screw (UPS) OLIF model, a bilateral CBT (BCBT) OLIF model, and an unilateral CBT(UCBT) OLIF model. The range of motion (ROM), as well as stress levels in the cage, L4 lower endplate, L5 upper endplate, and fixation constructs were assessed across these different model configurations.ResultsS-A model had the highest average ROM of six motion modes, followed by LSR, UPS, UCBT, BPS and BCBT. The BCBT model had a relatively lower cage stress than the others. The maximum peak von Mises stress of the fixation constructs was found in the LSR model. The maximum peak von Mises stress of L4 lower endplate was found in the S-A model. The peak von Mises stress on the L4 lower endplate of the rest surgical models showed no significant difference. The maximum peak von Mises stress of the L5 upper endplate was found in the S-A model. The minimum peak von Mises stress of the L5 upper endplate was found in the BCBT model. No significant difference was found for the peak von Mises stress of the L5 upper endplate among LSR, BPS, UPS and UCBT models.ConclusionAmong the six different fixation techniques, BCBT exhibited superior biomechanical stability and minimal stress on the cage-endplate interface. It was followed by BPS, UCBT, UPS, and LSR in terms of effectiveness. Conversely, S-A OLIF demonstrated the least stability and resulted in increased stress on both the cage and endplates. Combining OLIF with BCBT fixation technique enhanced biomechanical stability compared to BPS and presented as a less invasive alternative treatment for patients with degenerative lumbar spondylolisthesis.

Calculation of coal cleats spacing for methane degassing by image processing

Coal bed methane is one of the clean energy sources in the world. Methane molecules are confined inside the pores of coals and when the gas drainage wells are drilled into the coal seams, due to the resulted pressure difference. Coal cleats are spread all over the coal seam as face and butt cleats and play an essential role in the methane gas drainage operation from coal mines. Calculating the coal cleats spacing makes great help in modeling the amount of emitted gas followed by calculating the spacing of gas suction wells. Furthermore, the time required for gas drainage will be determined. Computer-based image processing technique has been utilized to identify the cleats spacing. In this study, three-dimensional computed tomography scan images were prepared for coal samples from Tabas Coal Mine in Iran. Computer-based image processing technology was conducted to determine the coal cleats. Their spacing was then calculated. Based on the results obtained using image processing with computer, the average distance between face cleats was 20 to 30 mm and the distance between butt cleats was 15 to 25 mm. The results of this study are used to modeling the amount of methane gas in the coal cleats. Based on this modeling, the amount of released gas and the distance between the wells can be designed.