Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review.

Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in "detection", "classification" and "segmentation" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96% in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.

Prediction of substantial closed-globe injuries in orbital wall fractures.

PURPOSE: To determine risk factors for substantial closed-globe injuries in orbital fractures (SCGI) and to develop the best multivariate model for the prediction of SCGI. METHODS: A retrospective study was performed on patients diagnosed with orbital fractures at Farabi Hospital between 2016 and 2022. Patients with a comprehensive ophthalmologic examination and orbital CT scan were included. Predictive signs or imaging findings for SCGI were identified by logistic regression (LR) analysis. Support vector machine (SVM), random forest regression (RFR), and extreme gradient boosting (XGBoost) were also trained using a fivefold cross-validation method. RESULTS: A total of 415 eyes from 403 patients were included. Factors associated with an increased risk of SCGI were reduced uncorrected visual acuity (UCVA), increased difference between UCVA of the traumatic eye from the contralateral eye, older age, male sex, grade of periorbital soft tissue trauma, trauma in the occupational setting, conjunctival hemorrhage, extraocular movement restriction, number of fractured walls, presence of medial wall fracture, size of fracture, intraorbital emphysema and retrobulbar hemorrhage. The area under the curve of the receiver operating characteristic for LR, SVM, RFR, and XGBoost for the prediction of SCGI was 57.2%, 68.8%, 63.7%, and 73.1%, respectively. CONCLUSIONS: Clinical and radiographic findings could be utilized to efficiently predict SCGI. XGBoost outperforms the logistic regression model in the prediction of SCGI and could be incorporated into clinical practice.

Machine learning applications for early detection of esophageal cancer: a systematic review.

INTRODUCTION: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. METHODS: We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review. RESULTS: The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. CONCLUSION: Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

Teleburn: Designing A Telemedicine Application to Improve Burn Treatment.

BACKGROUND: Due to the increasing rate of the burn injuries and a limited number of specialized treatment centers, providing medical advice and medical care at the point of need is necessary. The aim of the present study was to design and implement a teleburn system to enhance the quality of care for the burn patients. METHODS: This study was completed in 2016. In order to design the system, information needs assessment was conducted by using a questionnaire. The participants of this phase were five specialists, five general practitioners, and 12 nurses. The setting of the study was the burn department of a public hospital and a burn center. The prototype of the system was designed based on the findings derived from the first phase, and the usability of the system was evaluated later. RESULTS: The teleburn system was a web-based system with different sections for GPs/nurses and specialists. In total, 28 burn consultations were made successfully by using the system. The findings of the usability testing showed that most of the participants evaluated the system at a good level. The mean score for the specialists, general practitioners and nurses was 8.4±0.46, 7.7±0.39, and 7.5±0.51, respectively. CONCLUSION: Although it was the first time in the country that the teleburn system was designed and introduced to the clinicians, they seemed to be satisfied with using the system. This system could help general practitioners and nurses to receive specialist's advice on a timely manner to improve the treatment of the burn patients. However, more research should be conducted to determine the effectiveness of using this technology in the real work environment.