The adaptive nature of artificial intelligence (AI), with its ability to improve performance through continuous learning, offers substantial benefits across various sectors. However, current regulatory frameworks are not intended to accommodate this adaptive nature, and they have prolonged approval timelines, sometimes exceeding one year for some AI-enabled devices. This creates significant challenges for manufacturers who must deal with lengthy waits and submit multiple approval requests for AI-enabled device software functions as they are updated. In response, regulatory agencies like the US Food and Drug Administration (FDA) have introduced guidelines to better support the approval process for continuously evolving AI technologies. This article explores the FDA’s concept of predetermined change control plans and how they can streamline regulatory oversight by reducing the need for repeated approvals, while ensuring safety and compliance. This can help reduce the burden for regulatory bodies and decrease waiting times for approval decisions, therefore fostering innovation, increasing market uptake, and exploiting the benefits of artificial intelligence and machine learning technologies.

One of the complex challenges faced presently by tissue engineering (TE) is the development of vascularized constructs that accurately mimic the extracellular matrix (ECM) of native tissue in which they are inserted to promote vessel growth and, consequently, wound healing and tissue regeneration. TE technique is characterized by several stages, starting from the choice of cell culture and the more appropriate scaffold material that can adequately support and supply them with the necessary biological cues for microvessel development. The next step is to analyze the attained microvasculature, which is reliant on the available labeling and microscopy techniques to visualize the network, as well as metrics employed to characterize it. These are usually attained with the use of software, which has been cited in several works, although no clear standard procedure has been observed to promote the reproduction of the cell response analysis. The present review analyzes not only the various steps previously described in terms of the current standards for evaluation, but also surveys some of the available metrics and software used to quantify networks, along with the detection of analysis limitations and future improvements that could lead to considerable progress for angiogenesis evaluation and application in TE research.

Background: Retinal diseases are emerging as a significant health concern, making early detection and prompt treatment crucial to prevent visual impairment. Optical coherence tomography (OCT) is the preferred imaging modality for non-invasive diagnosis. Both diabetic macular edema (DME) and macular edema secondary to retinal vein occlusion (RVO) present an increase in retinal thickness, posing etiologic diagnostic challenges for non-specialists in retinal diseases. The lack of research on deep learning classification of macular edema secondary to RVO using OCT images motivated us to propose a convolutional neural network model for this task. Methods: The VGG-19 network was fine-tuned with a public dataset to classify OCT images. This network was then used to develop three models: unimodal—the input is only the OCT B-scan; multimodal—the inputs are the OCT B-scan and diabetes information, and multi-image—the inputs are the OCT B-scan, the infrared image, and the diabetes information. Seven hundred sixty-six patients from ULS São João were selected, comprising 208 healthy eyes, 207 with macular edema secondary to RVO, 218 with DME, and 200 with other pathologies. The performance metrics include accuracy, precision, recall, F_0.5 score, and area under the receiver operating characteristic curve (AUROC). Results: The multi-image model achieved better results, with an accuracy of 95.20%, precision of 95.43%, recall of 95.20%, F_0.5-score of 95.32%, F₁-score of 95.21%, and AUROC of 99.59% on the classification task between four classes. Conclusions: This study presents a novel method to distinguish macular edema secondary to RVO and DME using diabetes diagnosis, OCT, and infrared images. This research aims to provide a reliable tool for ophthalmologists, improving the accuracy and speed of diagnoses.