Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set

Deep learning based dual stage model for accurate nasogastric tube positioning in chest radiographs

Accurate placement of nasogastric tubes (NGTs) is crucial for ensuring patient safety and effective treatment. Traditional methods relying on manual inspection are susceptible to human error, highlighting the need for innovative solutions. This study introduces a deep-learning model that enhances the detection and analysis of NGT positioning in chest radiographs. By integrating advanced segmentation and classification techniques, the model leverages the nnU-Net framework for segmenting critical regions and the ResNet50 architecture, pre-trained with MedCLIP, for classifying NGT placement. Trained on 1799 chest radiographs, the model demonstrates remarkable performance, achieving a Dice Similarity Coefficient of 65.35% for segmentation and an Area Under the Curve of 99.72% for classification. These results underscore its ability to accurately distinguish between correct and incorrect placements, outperforming traditional approaches. This method not only enhances diagnostic precision but also has the potential to streamline clinical workflows and improve patient care. A functional prototype of the model is accessible at https://ngtube.ziovision.ai .

Advancing Diabetic Retinopathy Diagnosis: Leveraging Optical Coherence Tomography Imaging with Convolutional Neural Networks

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and accurate diagnosis. The combination of optical coherence tomography (OCT) imaging with convolutional neural networks (CNNs) has emerged as a promising approach for enhancing DR diagnosis. OCT provides detailed retinal morphology information, while CNNs analyze OCT images for automated detection and classification of DR. This paper reviews the current research on OCT imaging and CNNs for DR diagnosis, discussing their technical aspects and suitability. It explores CNN applications in detecting lesions, segmenting microaneurysms, and assessing disease severity, showing high sensitivity and accuracy. CNN models outperform traditional methods and rival expert ophthalmologists' results. However, challenges such as dataset availability and model interpretability remain. Future directions include multimodal imaging integration and real-time, point-of-care CNN systems for DR screening. The integration of OCT imaging with CNNs has transformative potential in DR diagnosis, facilitating early intervention, personalized treatments, and improved patient outcomes. Abbreviations: DR = Diabetic Retinopathy, OCT = Optical Coherence Tomography, CNN = Convolutional Neural Network, CMV = Cytomegalovirus, PDR = Proliferative Diabetic Retinopathy, AMD = Age-Related Macular Degeneration, VEGF = vascular endothelial growth factor, RAP = Retinal Angiomatous Proliferation, OCTA = OCT Angiography, AI = Artificial Intelligence.

Artificial intelligence & clinical nutrition: What the future might have in store

Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interventions for patients. Clinical nutrition is a critical aspect of patient care, and AI can help clinicians make more informed decisions regarding patients' nutritional requirements, disease prevention, and management. AI algorithms can analyse large datasets to identify novel associations between diet and disease outcomes, enabling clinicians to make evidence-based nutritional recommendations. AI-powered devices and applications can also assist in tracking dietary intake, providing feedback, and motivating patients towards healthier food choices. However, the adoption of AI in clinical nutrition raises several ethical and regulatory concerns, such as data privacy and bias. Further research is needed to assess the clinical effectiveness and safety of AI-powered nutrition interventions. In conclusion, AI has the potential to transform clinical nutrition, but its integration into clinical practice should be carefully monitored to ensure patient safety and benefit. This article discusses the current and future applications of AI in clinical nutrition and highlights its potential benefits.