Accurate Diagnosis Research Articles

Эректильная дисфункция: современное состояние проблемы, методы диагностики и рентгенэндохирургического лечения

According to the Russian Society of Urologists, almost half of men aged 27 to 77 suffer from erectile dysfunction. ED should be considered not only as a medical nosology, but also as a socio-psychological problem with a demographic aspect. There are many methodsfor improving erectile function, including psychological consultations, drug therapy and penile prosthetics. Given the accumulated international experience,the key pointis an accurate diagnosis ofthe cause of erectile dysfunction to determine the correct approach to treatment. Among the many causes of erectile dysfunction, vascular causes are quite common. Given the rapid development of endovascular and low-traumatic approachesto vascular pathology,the technique of improving inflow and outflow obstruction in vasculogenic erectile dysfunction is becoming increasingly obvious and effective. The presented article discussesthe currentstate ofthe issue and possible prospectsfor endovascularsurgery for vascular erectile dysfunction, including significant venous leakage.

Investigating the genetic basis of hereditary spastic paraplegia and cerebellar Ataxia in Pakistani families

BackgroundHereditary Spastic Paraplegias (HSPs) and Hereditary Cerebellar Ataxias (HCAs) are progressive neurodegenerative disorders encompassing a spectrum of neurogenetic conditions with significant overlaps of clinical features. Spastic ataxias are a group of conditions that have features of both cerebellar ataxia and spasticity, and these conditions are frequently clinically challenging to distinguish. Accurate genetic diagnosis is crucial but challenging, particularly in resource-limited settings. This study aims to investigate the genetic basis of HSPs and HCAs in Pakistani families.MethodsFamilies from Khyber Pakhtunkhwa with at least two members showing HSP or HCA phenotypes, and who had not previously been analyzed genetically, were included. Families were referred for genetic analysis by local neurologists based on the proband’s clinical features and signs of a potential genetic neurodegenerative disorder. Whole Exome Sequencing (WES) and Sanger sequencing were then used to identify and validate genetic variants, and to analyze variant segregation within families to determine inheritance patterns. The mean age of onset and standard deviation were calculated to assess variability among affected individuals, and the success rate was compared with literature reports using differences in proportions and Cohen’s h.ResultsPathogenic variants associated with these conditions were identified in five of eight families, segregating according to autosomal recessive inheritance. These variants included previously reported SACS c.2182 C > T, p.(Arg728*), FA2H c.159_176del, p.(Arg53_Ile58del) and SPG11 c.2146 C > T, p.(Gln716*) variants, and two previously unreported variants in SACS c.2229del, p.(Phe743Leufs*8) and ZFYVE26 c.1926_1941del, p.(Tyr643Metfs*2). Additionally, FA2H and SPG11 variants were found to have recurrent occurrences, suggesting a potential founder effect within the Pakistani population. Onset age among affected individuals ranged from 1 to 14 years (M = 6.23, SD = 3.96). The diagnostic success rate was 62.5%, with moderate effect sizes compared to previous studies.ConclusionsThe findings of this study expand the genotypic and phenotypic spectrum of HSPs and HCAs in Pakistan and emphasize the importance of utilizing exome/genome sequencing for accurate diagnosis or support accurate differential diagnosis. This approach can improve genetic counseling and clinical management, addressing the challenges of diagnosing neurodegenerative disorders in resource-limited settings.

Early removal of transorbital penetrating traumatic brain injury by a wooden object: a case report

Introduction and Importance: Transorbital penetrating traumatic brain injury (TPTBI) is a rare cause of penetrating head injury, yet the diagnoses can be overlooked in some cases due to incomplete history, trivial trauma, and the absence of immediate neurologic deficits. Case Presentation: A 4-year-old male child was admitted with a diagnosis of TPTBI by a wooden object. Non-contrast enhanced CT scan revealed a linear hypodense structure extending from the superior wall of the right orbit into the frontal lobe with displaced fragments into the brain. Additionally, cerebral contusion, pneumocephalus, and hematoma with surrounding edema were seen in the frontal lobe. Early extraction of the foreign body was performed by a multidisciplinary team of Neurosurgeons and Ophthalmologists. The wooden fragment of size 10 cm×1 cm×1 cm was removed by a small mini-craniotomy of the right frontal supraorbital bone with the gentle pulling of it through the dura mater and frontal cortex. The postoperative course was uncomplicated, vision was restored, and there were no neurologic deficits at the time of discharge on the 7th postoperative day with good postoperative outcomes. Clinical Discussion: Delayed removal of TPTBI may be complicated by misidentification of entry points, extensive scarring around the foreign body, adherence to the intracranial vessels, and increasing mortality due to infectious complications. Therefore, early exploration with the help of a multidisciplinary team will most likely result in successful debridement, good neurologic outcomes, and lower morbidity and mortality. Conclusion: Accurate diagnosis and early intervention for TPTBI may prevent possible infectious and neurologic complications.

Implementasi Data Mining Klasifikasi Gejala Penyakit TB Menggunakan Algoritma Naive Bayes pada Studi Kasus Puskesmas Pegangsaan Dua B

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. The bacteria can enter the lungs, causing the patient to experience shortness of breath and chronic coughing. The infection process in TB progresses through three main stages: primary infection, latent infection, and active infection. During the primary infection stage, the bacteria enter the lungs through inhaled air. If the immune system successfully controls the bacteria, the infection becomes latent, where the bacteria remain inactive and cause no symptoms. However, if the immune system fails, the infection becomes active, and the bacteria attack healthy lung cells, leading to severe symptoms. Tuberculosis remains a major cause of global morbidity and mortality. According to the 2022 WHO report, Indonesia ranks second in the world with an estimated 969,000 TB cases, or 354 per 100,000 people, and a mortality rate of 144,000, or 52 per 100,000 people. This study aims to improve the accuracy of TB diagnosis through the application of the Naive Bayes algorithm, applied to patient data from the Pegangsaan Dua B Community Health Center. The dataset includes 1,527 training data and 1,029 testing data, covering attributes such as patient name, gender, age, identification number, registration date, address, diagnosis status, and diagnosis results. The results indicate that the Naive Bayes algorithm achieved an accuracy of 99.71%, showing its potential for early and accurate TB diagnosis prediction.

Application of improved adaptive method of CEEMD and data-drive in fault diagnosis

Purpose The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis. Design/methodology/approach This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis. Findings According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate. Research limitations/implications In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied. Originality/value Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Fault Diagnosis for Multiple Redundancy Aileron Actuator based on Parallel-SDP and Polar Sparse Representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.
First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.

Emerging public health strategies in malaria control: innovations and implications

Malaria remains a significant global health challenge, particularly in regions with limited resources and tropical climates. Despite extensive efforts, the disease continues to cause significant morbidity and mortality, with approximately 229 million cases and 409,000 deaths reported in 2020. However, recent years have seen promising advancements in public health strategies aimed at malaria control and elimination. Technological advancements have played a crucial role in improving malaria control efforts. Genomic surveillance techniques enable the monitoring of malaria parasite populations, aiding in the detection of drug resistance and informing targeted interventions. Additionally, innovative diagnostic technologies, such as rapid diagnostic tests (RDTs) and molecular assays, have enhanced the speed and accuracy of malaria diagnosis, facilitated prompt treatment and reduced transmission. These tools are instrumental in achieving the World Health Organization’s (WHO) goals of reducing malaria cases and deaths by at least 90% by 2030. Novel vector control methods offer innovative approaches to reduce malaria transmission. Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) remain foundational strategies, with advancements including the development of next-generation insecticides and long-lasting insecticidal nets (LLINs). Furthermore, genetic modification of mosquitoes, such as gene drive technology, holds promise for reducing mosquito populations and interrupting malaria transmission. These vector control innovations complement other strategies, contributing to comprehensive malaria control efforts aimed at achieving sustainable disease reduction and eventual elimination.

Review of medical image processing using quantum-enabled algorithms

Efficient and reliable storage, analysis, and transmission of medical images are imperative for accurate diagnosis, treatment, and management of various diseases. Since quantum computing can revolutionize big data analytics by providing faster solutions and security tactics, numerous studies in this field have focused on the use of quantum and quantum-inspired algorithms to enhance the performance of traditional medical image processing approaches. This review aims to provide readers with a succinct yet adequate compendium of the advances in medical image processing combined with quantum behaviors for disease diagnosis and medical image security. Some open challenges are outlined, identifying the performance limitations of current quantum technology in their applications, while addressing the short-, medium-, and long-term development plans of this field in designing future quantum healthcare systems. We hope that this review will provide full guidance for upcoming researchers interested in this area and will stimulate further appetite of experts already active in this area aimed at the pursuit of more advanced quantum paradigms in medical image processing applications.

Permanent magnet synchronous motor demagnetization fault diagnosis based on PCA-ISSA-PNN

Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA) algorithm, the improved sparrow search algorithm (ISSA), and the probabilistic neural network (PNN) algorithm is proposed. First, the principal components of phase currents are extracted using PCA. Second, ISSA is used to optimize the smoothing coefficients of the PNN algorithm, and the optimized PNN algorithm is combined with PCA to obtain the PCA-ISSA-PNN fault diagnosis model. Finally, the established fault diagnosis model was tested using the current data collected from the experiments and compared with the fault diagnosis indexes and optimization performance of the conventional PNN, PCA-PNN, PCA-GA (genetic algorithm)-PNN, PCA-DA (dragonfly algorithm)-PNN, PCA-GTO (artificial gorilla troop optimizer)-PNN, PCA-AHA-PNN, and PCA-SSA-PNN. The test results show that the fault diagnosis accuracy of PCA-ISSA-PNN reaches 95.83%, and the fault diagnosis indexes are significantly higher than those of PNN, PCA-PNN, PCA-GA-PNN, and PCA-DA-PNN; its optimization performance is also significantly better than that of PCA-GTO-PNN, PCA-AHA-PNN, and PCA-SSA-PNN, which verifies the accuracy and efficiency of the proposed method.

Clinical presentation, imaging characteristics, and short‐term outcome of eight cats presented with suspected traumatic atlantoaxial hyperflexion

AbstractTraumatic atlantoaxial hyperflexion is considered rare in cats, and only a few case reports have been published. There are other conditions that can result in a peracute onset of neurological signs localized to the cervical spinal cord segments, including acute noncompressive nucleus pulposus extrusion, ischemic myelopathy, or vertebral fractures. Appropriate treatment for these conditions can only be initiated after an accurate diagnosis has been obtained. The aim of this observational, retrospective, single‐center, descriptive case series study was to describe the clinical presentation, imaging characteristics, and short‐term outcome of eight cats presented with suspected traumatic atlantoaxial hyperflexion. Young male healthy domestic shorthair cats were overrepresented (7/8) and typically presented with a peracute, nonprogressive, nonpainful, nonlateralizing C1–C5 myelopathy (tetraplegia or nonambulatory tetraparesis) following a road traffic accident or head trauma. All MRI studies demonstrated a solitary, focal, ill‐defined intramedullary lesion immediately dorsal to the dens of the axis, affecting both grey and white matter. All cats were treated medically. In 50% of the cats, the neurological grade improved at discharge or short‐term follow‐up, 25% of the cats recovered completely, one cat was static at short‐term follow‐up, and one cat was euthanized due to persistent forebrain signs and lack of neurological improvement.

Improving treatment plan and mental health in children with abdominal infection for broad-spectrum bacterial infections

BACKGROUND Pediatric abdominal infection is a common but serious disease that requires timely and effective treatment. In surgical treatment, accurate diagnosis and rational application of antibiotics are the keys to improving treatment effects. AIM To investigate the effect of broad-spectrum bacterial detection on postoperative antibiotic therapy. METHODS A total of 100 children with abdominal infection who received surgical treatment in our hospital from September 2020 to July 2021 were grouped. The observation group collected blood samples upon admission and sent them for broad-spectrum bacterial infection nucleic acid testing, and collected pus or exudate during the operation for bacterial culture and drug sensitivity testing; the control group only sent bacterial culture and drug sensitivity testing during the operation. RESULTS White blood cell count, C-reactive protein, procalcitonin, 3 days after surgery, showed better postoperative index than the control group (P < 0.05). The hospital stay in the observation group was significantly shorter than that in the control group. The hospitalization cost in the observation group was significantly lower than that in the control group, and the difference between the two groups was statistically significant (P < 0.05). CONCLUSION Early detection of broad-spectrum bacterial infection nucleic acids in pediatric abdominal infections can help identify pathogens sooner and guide the appropriate use of antibiotics, improving treatment outcomes and reducing medical costs to some extent.

Diagnostic and Predictive Immunocytochemistry in Lung Cancer

Background Immunocytochemistry (ICC) is suitable for use on a range of cytology preparations, such as cell blocks, air-dried slides, ethanol-fixed slides, direct smears, cytospins, and liquid-based cytology (LBC) samples. However, it must be standardized against the gold standard of formalin-fixed paraffin-embedded tissues with adequate number of positive and negative controls. The role of ICC in lung cancer is crucial, as most lung cancer specimens are cytology samples. Accurate diagnosis and testing of certain biomarkers rely heavily on both diagnostic and predictive ICC. Summary Key ICC markers important in lung cancer include, but are not limited to, diagnostic ICCs such as TTF-1, p40, Napsin A, and p63, as well as predictive ICCs like ALK, ROS-1, PD-L1, and NTRK. Key Messages With proper validation, immunocytochemistry for lung cancer can be effectively performed on direct smears, cytospins, and other specimens, even when resources for preparing cell blocks are unavailable. This is particularly true for diagnostic antibodies, but it is important to exercise caution with predictive immunocytochemistry. Nonetheless, a low threshold for molecular testing should be maintained. PD-L1 ICC can be challenging and should ideally be performed on formalin-fixed cell blocks or biopsies when available.

Artificial intelligence in paediatric head trauma: enhancing diagnostic accuracy for skull fractures and brain haemorrhages.

Pediatric head trauma is a significant cause of morbidity and mortality, with children, particularly those under two years old, being more susceptible to skull fractures due to their unique physiological and developmental characteristics. A recent study by Azusa Ono et al. examined the impact of repeated imaging in children under 24 months with minor head trauma, revealing that 40.6% of those who underwent follow-up MRI after an initial CT scan showed new intracranial findings. The study emphasizes the importance of careful consideration of repeated imaging based on initial findings and associated risk factors, such as the presence of subcutaneous hematoma and fractures intersecting coronal sutures. This underscores the need for improved diagnostic approaches to minimize radiation exposure while ensuring accurate diagnosis.Artificial Intelligence (AI) offers a promising solution, with research indicating that AI models can significantly improve diagnostic precision, increasing accuracy from 78.1 to 85.2% and reducing errors by two to three times. Additionally, AI has demonstrated high accuracy in detecting various types of brain hemorrhages, potentially facilitating earlier and more precise detection of hematomas associated with skull fractures. Integrating AI into diagnostic practices could enhance early detection, reduce diagnostic errors, and improve outcomes for pediatric head trauma cases. The study underscores the critical need for advanced diagnostic methods to better manage and treat head injuries in young children, where timely and accurate diagnosis is crucial.

Evaluation of Chemokines MIG and IP-10 as Immunological Biomarkers of Human Visceral Leishmaniasis: A Systematic Review

Visceral leishmaniasis (VL) is a neglected tropical disease that is potentially fatal when untreated. Current diagnostic methods have limitations that contribute to ongoing transmission and poor prognosis. Thus, new tests are needed to provide quick, accurate diagnoses and evaluate clinical progression and treatment efficacy. The monokine induced by interferon-gamma (MIG) and interferon-gamma-inducible protein 10 (IP-10) has been associated with the host susceptibility to VL with potential diagnostic and prognostic purposes. We performed a systematic review using four search databases (Scopus, PubMed, Web of Science, and MEDLINE) to identify studies assessing MIG and IP-10 as potential biomarkers in patients with VL across various clinical conditions. A total of 13 studies were potentially eligible and included in this review. The articles, in general, reveal that the chemokines MIG and IP-10 are elevated in response to infection by Leishmania spp., acting on the host’s resistance to the development of the disease. They are associated with asymptomatic conditions and after VL treatment, and this relationship can be observed in both immunocompetent and immunocompromised individuals. Consequently, these chemokines hold relevance in the diagnoses and appropriate management of individuals with VL.

A fault diagnosis method for analog circuits based on EEMD-PSO-SVM

Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods.

Kniest Dysplasia without Ocular and Auditory Abnormalities in a Boy of 12 Months

Introduction: Kniest dysplasia is a rare skeletal disorder, characterized by mild dysmorphic features, cleft palate, short stature, short limbs, prominent joints, restricted joint mobility, hearing impairment, and ocular manifestations such as high-degree myopia, retinal detachment, and cataract. Typical radiological findings include platyspondyly, coronal clefts, and dumbbell-shaped long tubular bones. Case Presentation: Herein, we report on an 8-month-old boy who was referred to the pediatric genetic department due to narrow thorax and short extremities. He had mild dysmorphic features, cleft palate, narrow thorax, short extremities, and short stature. On radiographies, platyspondyly, hemivertebra, and dumbbell-shaped long tubular bones were detected. Clinical and radiological findings were consistent with Kniest dysplasia. Clinical exome sequencing was performed and revealed a heterozygous, pathogenic c.905C>T (p.Ala302Val) variant in the COL2A1 gene, confirming the initial clinical diagnosis. Discussion: Kniest dysplasia is a very rare skeletal dysplasia, and an accurate clinical diagnosis is important to provide the best possible follow-up.

Detection of hypertension using a target spectral camera: a prospective clinical study

Hypertension is a significant contributor to premature mortality, and the regular monitoring of blood pressure (BP) enables the early detection of hypertension and cardiovascular disease. There is an urgent need for the development of highly accurate cuffless BP devices. We examined BP measurements based on a target spectral camera’s recordings and evaluated their accuracy. Images of 215 adults’ palms and faces were recorded, and BP was measured. The camera captured RGB wavelength data at 640 × 480 pixels and 150 frames per second (fps). These recordings were analyzed to extract pulse transit time (PTT) values between the face and palm, a key parameter for estimating BP. Continuous BP measurements were taken using a CNAPmonitor500 for validation. Three frequency wavelengths were measured from video images. A machine learning model was constructed to determine hypertension, defined as a systolic BP of 130 mmHg or higher or a diastolic BP of 80 mmHg or higher, using the visualized data. The discrimination between hypertension and normal BP was 95.0% accurate within 30 s and 90.3% within 5 s, based on the captured images. The results of heartbeat-by-heartbeat analyses can be used to determine hypertension based on only one second of camera footage or one heartbeat. The data extracted from a video recorded by a target spectral camera enabled accurate hypertension diagnoses, suggesting the potential for simplified BP monitoring.

Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.

The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care disease diagnosis. However, the low sensitivity and insufficient accuracy still limit its clinical application. In this work, a machine learning (ML)-optimized colorimetric LFA with ultrasound enrichment is developed to achieve the sensitive and accurate detection of tau proteins for early screening of Alzheimer's disease (AD). The LFA device is integrated with a portable ultrasonic actuator to rapidly enrich microparticles using ultrasound, which is essential for sample pre-enrichment to improve the sensitivity, followed by ML algorithms to classify and predict the enhanced colorimetric signals. The results of the undiluted serum sample testing show that the protocol enables efficient classification and accurate quantification of the AD biomarker tau protein concentration with an average classification accuracy of 98.11% and an average prediction accuracy of 99.99%, achieving a limit of detection (LOD) as sensitive as 10.30pgmL-1. Further point-of-care testing (POCT) of human plasma samples demonstrates the potential use of LFA in clinical trials. Such a reliable lateral flow immunosensor with high precision and superb sensing performance is expected to put LFA in perspective as an AD clinical diagnostic platform.

Monostotic fibrous dysplasia of jaw bones: a case series

Abstract Background Fibrous dysplasia (FD) is a benign fibro-osseous lesion, a skeletal developmental anomaly of the bone-forming mesenchyme. The diagnosis of fibro-osseous lesions, particularly those of the jaw bones, poses significant challenges to clinicians and pathologists since it requires a correlation of clinical, radiological, histological, and surgical findings. Accurate and specific diagnosis is crucial as treatment modalities differ with different fibro-osseous lesions. Methods This retrospective analysis presents a case series of a rare condition of monostotic FD in the maxillofacial region affecting jaw bones diagnosed and/or treated over period of 10 years. Results Five cases of monostotic FD were diagnosed and treated between a period of 2013 and 2023. The cases from the 2nd to 8th decade were included in the analysis with equal involvement of males and females. Out of five cases, four cases were involving maxilla and 1 showed involvement of mandible. Conclusion FD is a rare entity affecting the jaw bones which often lead to disfigurement of face. Early detection is warranted to decrease potential complications. In addition, genetic analysis could help in understanding the occurrence in certain population.

Experimental Investigation Using Robust Deep VMD-ICA and 1D-CNN for Condition Monitoring of Roller Element Bearing

Abstract A rotor-bearing system experiences numerous vibrations during the operation that frequently degrade performance and endanger operational safety. Roller bearing failure has significant consequences, leading to downtime or even a complete outage of rotating machinery. It is crucial to detect and diagnose incipient bearing defects promptly to ensure optimal operation of the machinery and minimize potential disruptions to the process. Deep Independent Component Analysis is a necessity used in modern condition monitoring to detect bearing failure earlier than it occurs. To address this issue the feasibility of utilizing Deep Independent Component Analysis (ICA) method based on Variational Modal Decomposition (VMD) with one-dimensional convolutional neural network (1D-CNN) to diagnose incipient bearing defect. Fast Fourier Techniques are utilized to extract the vibration signatures of artificially damaged bearings on a newly built test bed. VMD addresses to minimize data noise, allowing data to decompose into various sub-datasets for extraction of incipient defect features. With weak defect characteristic signal and noise interference, the Deep VMD-ICA model and 1D-CNN simplicity improved the accuracy of diagnosis corresponding to the experimental results. Moreover, Deep VMD-ICA with 1D-CNN has additionally demonstrated strong performance in comparison to experimental results and is useful for monitoring the condition of industrial machinery. The results reveal that this fault diagnosis approach is reliable, with a diagnostic accuracy of 98.93% for bearing faults.