Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning

Advances in artificial intelligence models and algorithms in the field of optometry

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models

Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread. Artificial intelligence is one of the approaches commonly used by researchers to discover the problems it causes and provide solutions. These estimates would help enable health systems to take the necessary steps to diagnose and track cases of COVID. In this review, we intend to offer a novel method of automatic detection of COVID-19 using tomographic images (CT) and radiographic images (Chest X-ray). In order to improve the performance of the detection system for this outbreak, we used two deep learning models: the VGG and ResNet. The results of the experiments show that our proposed models achieved the best accuracy of 99.35 and 96.77% respectively for VGG19 and ResNet50 with all the chest X-ray images.

Predicting the biomechanical stability of IOLs inside the postcataract capsular bag with a finite element model

BACKGROUND AND OBJECTIVES

Although cataract surgery is a safe operation in developed countries, there is still room for improvement in terms of patient satisfaction. One of the key issues is assessing the biomechanical stability of the IOL within the capsular bag to avoid refractive errors that lead to a second surgery. For that purpose, a numerical model was developed to predict IOL position inside the capsular bag in the short- and long-term.

METHODS

A finite element model containing the implanted IOL, the postcataract capsular bag, the zonules, and a portion of the ciliary body was designed. The C-loop hydrophobic LUCIA IOL was used to validate the numerical model and two more worldwide IOL designs were tested: the double C-loop hydrophobic POD FT IOL and the plate hydrophilic AT LISA IOL. To analyze the biomechanical stability in the long-term, the effect of the fusion footprint, which occurs days following cataract surgery, was simulated. Moreover, several scenarios were analyzed: the size and location of the capsulorexhis, the capsular bag diameter, the initial geometry of the capsular bag, and the material properties of the bag.

RESULTS

The biomechanical stability of the LUCIA IOL was simulated and successfully compared with the in vitro results. The plate AT LISA design deformed the capsular bag diameter up to 11.0 mm against 10.5 mm for the other designs. This design presented higher axial displacement and lower rotation, 0.19 mm and 0.2^∘, than the C-loop design, 0.09 mm and 0.9^∘.

CONCLUSIONS

All optomechanical biomarkers were optimal, assuring good optical performance of the three IOLs under investigation. Our findings showed that the capsulorexhis size influences the stiffness of the capsular bag; however, the shape in the anterior and posterior curvature surfaces of the bag barely affect. The results also suggested that the IOL is prone to mechanical perturbations with the fusion footprint, but they were not high enough to produce a significant refractive error. The proposed model could be a breakthrough in the selection of haptic design according to patient criteria.

Automated segmentation of the ciliary muscle in OCT images using fully convolutional networks

Quantifying shape changes in the ciliary muscle during accommodation is essential in understanding the potential role of the ciliary muscle in presbyopia. The ciliary muscle can be imaged in-vivo using OCT but quantifying the ciliary muscle shape from these images has been challenging both due to the low contrast of the images at the apex of the ciliary muscle and the tedious work of segmenting the ciliary muscle shape. We present an automatic-segmentation tool for OCT images of the ciliary muscle using fully convolutional networks. A study using a dataset of 1,039 images shows that the trained fully convolutional network can successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation. The study also shows that EfficientNet outperforms other current backbones of the literature.

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson's ρ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach.