Original Research
Abstract
Full TextPDF The use of artificial intelligence (AI) and machine learning (ML) in healthcare has seen significant growth in recent years. In this study, we explored the potential of deep learning techniques for dental teeth detection and the identification of teeth as “normal”, “implant”, “root”, “erupting”, and “missing” using X-ray images. Traditionally, dentists rely on visual-tactile methods to diagnose oral conditions. However, these methods have limitations, such as inefficiency in time spent on diagnosis, the high cost of diagnosis, and subjectivity in the diagnosis.
To address these limitations, we developed an automated algorithm that recognizes teeth structures using computer vision technology and ML methods. Our algorithm was trained on a dataset of 340 adult teeth X-ray radiographs and optimized through a series of experiments to determine the best training hyper-parameters.
The development of an AI-based clinical decision support system for dental diagnosis can increase efficiency and accuracy in clinical decision-making. Our study contributes to the field of dentistry by exploring the potential of AI and ML techniques for teeth recognition and detection. Additionally, we provided mathematical explanations of our observations to aid in the interpretation of our results.
Our optimized algorithm achieved a good precision of 78.2%. Overall, our study successfully demonstrated the potential of AI and ML in dental healthcare, specifically in teeth detection and implant identification using X-ray images.
Research Article
Abstract
Full TextPDF The respiratory system is not the only organ system affected by chronic obstructive pulmonary disease (COPD); muscle mass is also impacted. As skeletal and respiratory muscles deteriorate, ventilation, the severity of the illness, and clinical symptoms are all negatively impacted. According to recent studies, ultrasonography is a dependable and user-friendly method for determining the quadriceps contractile index (Qci) in patients with COPD.
Objective: Evaluate the Qci by ultrasound in patients with COPD and correlate with different clinical, 6-minute walk test (TM6), and ventilatory variables.
Methods: We conducted a prospective, observational study from 2021 to 2024. A total of sixty-one consecutive patients with spirometry-confirmed stable COPD were included after obtaining informed written consent. Demographic and clinical data, spirometric values, the modified Medical Research Council (mMRC) dyspnea score, TM6, COPD assessment test (CAT), Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification 2023, and measurement of Qci were collected for analysis. This is a prospective study conducted at Constantine Regional Military University Hospital.
Results: The mean total quadriceps muscle thickness (Qthick) in our patients with COPD is 1.95 ± 0.85 cm. The mean Qci in our patients with COPD is 68 ± 9%. A negative correlation has been reported between Qci and the dyspnea mMRC, P = 0,010. Patients in group E according to GOLD 2023 constitute more than half of our cohort, representing 54.1% of cases. A negative correlation has been reported between Qci and the stage of GOLD 2023 classification, P = 0,010. In our study, a correlation between the Qci and spirometric data (FVC, FEV1) was objectified with P < 0,001 and FEV1/FVC P = 0,008. The average distance travelled in the TM6 test was 470 129.37 m. Our study has shown that the more EXdi increases, the distance patients travel increases (P = 0,001).
Conclusion: In patients with COPD, the Qci appears to be associated with airway obstruction, TM6, CAT score, as well as perception of dyspnea.
Case Report
Abstract
Full TextPDF Bacterial keratitis is a serious and potentially sight-threatening complication most commonly associated with overnight contact lens wear. This case report presents the management of a 22-year-old male patient who developed bacterial keratitis following non-compliance with recommended contact lens care practices, specifically sleeping in contact lenses. The report discusses the diagnosis, differential diagnosis, and treatment, along with the risk factors associated with contact-lens-related microbial keratitis (CLMK). Early intervention with fortified antibiotics and patient education led to a quick resolution of the condition. This case emphasizes the importance of proper contact lens hygiene and the timely treatment of bacterial keratitis to prevent visual impairment.
