RESUMEN
ABSTRACT Objective: To study vertex-optical distance variation and estimate its impact on manifest refraction. Methods: Prospective study in a private clinic using the Vision-S™ 700 with five forehead positions. Forehead on the third position showed the closest vertex-optical distance of 12mm. Results: Analysis of 52 eyes from 26 patients revealed mean differences in vertex-optical distance of 12.25mm (right eye) and 11.75mm (left eye). A 2mm change in vertex-optical distance resulted in a 0.05D change for a 5D spherical equivalent and 0.20D for a 10D equivalent. Conclusion: Vertex-optical distance varies among patients and is influenced by forehead adjustment. These variations impact refraction accuracy and treatment evaluation. Adjusting the forehead to the third position on the Vision-S™ 700 is recommended.
RESUMO Objetivo: Estudar a variação da distância vértice-óptico, de acordo com o ajuste da testa, e estimar seu impacto na refração manifesta. Métodos: Estudo prospectivo realizado em clínica privada. A refração foi realizada utilizando cinco posições preestabelecidas com o Vision-Sa 700. A testa disposta na terceira posição apresentou distância vértice do refrator mais próxima de 12mm. Resultados: Foram analisados 52 olhos de 26 pacientes. A diferença média da distância vértice do refrator no olho direito foi de 12,25mm (variação de 11,50mm) e, no olho esquerdo, 11,75mm (variação de 12,00mm). O impacto foi de 2mm na distância vértice do refrator, fomentando em uma mudança de 0,05D para um equivalente esférico de 5D e 0,20D para um equivalente de 10D. Conclusão: A distância vértice do refrator varia entre pacientes, estando relacionada ao ajuste da testa. As variações afetam a precisão da refração, impactando no ajuste dos óculos, das lentes de contato e na avaliação pós-operatória de cirurgia refrativa. Sugerimos ajustar a posição da testa para terceira posição no Vision-S™ 700, se a distância vértice do refrator não for medida em todos os pacientes.
Asunto(s)
Humanos , Masculino , Femenino , Adolescente , Adulto , Persona de Mediana Edad , Optometría/instrumentación , Optometría/métodos , Refracción Ocular/fisiología , Pruebas de Visión/instrumentación , Pruebas de Visión/métodos , Lentes , Postura , Errores de Refracción , Cefalometría , Estudios Prospectivos , Procedimientos Quirúrgicos Refractivos , Óptica y Fotónica , Posicionamiento del PacienteRESUMEN
Simplices are used as building blocks to construct an interesting class of topological spaces called simplicial complexes, which exhibit remarkable symmetric properties. In this paper, we study the problems of inequalities for two simplices in the Euclidean space En. We establish some new inequalities for the vertex distances of two simplices and obtain some generalizations of the classical Neuberg-Pedoe inequality.
RESUMEN
Automatic medical image detection aims to utilize artificial intelligence techniques to detect lesions in medical images accurately and efficiently, which is one of the most important tasks in computer-aided diagnosis (CAD) systems, and can be embedded into portable imaging devices for intelligent Point of Care (PoC) Diagnostics. The Feature Pyramid Networks (FPN) based models are widely used deep-learning-based solutions for automatic medical image detection. However, FPN-based medical lesion detection models have two shortcomings: the object position offset problem and the degradation problem of IoU-based loss. Therefore, in this work, we propose a novel FPN-based backbone model, i.e., Multi-Pathway Feature Pyramid Networks with Position Attention Guided Connections and Vertex Distance IoU (abbreviated as PAC-Net), to replace vanilla FPN for more accurate lesion detection, where two innovative improvements, a position attention guided connection (PAC) module and Vertex Distance IoU Vertex Distance Intersection over Union loss, are proposed to address the above-mentioned shortcomings of vanilla FPN, respectively. Extensive experiments are conducted on a public medical image detection dataset, i.e., Deeplesion, and the results showed that i) PAC-Net outperforms all state-of-the-art FPN-based depth models in both evaluation metrics of lesion detection on the DeepLesion dataset, ii) the proposed PAC module and VDIoU loss are both effective and important for PAC-Net to achieve a superior performance in automatic medical image detection tasks, and iii) the proposed VDIoU loss converges more quickly than the existing IoU-based losses, making PAC-Net an accurate and also highly efficient 3D medical image detection model.