Application of artifcial intelligence in cataract management:current and future directions

The rise of artifcial intelligence(AI)has brought breakthroughs in many areas of medicine.In ophthalmology,AI has delivered robust results in the screening and detection of diabetic retinopathy,age-related macular degeneration,glaucoma,and retinopathy of prematurity.Cataract management is another feld that can beneft from greater AI application.Cataract is the leading cause of reversible visual impairment with a rising global clinical burden.Improved diagnosis,monitoring,and surgical management are necessary to address this challenge.In addition,patients in large developing countries often sufer from limited access to tertiary care,a problem further exacerbated by the ongoing COVID-19 pandemic.AI on the other hand,can help transform cataract management by improving automation,efcacy and overcoming geographical barriers.First,AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs.This utilizes a deep-learning,convolutional neural network(CNN)to detect and classify referable cataracts appropriately.Second,some of the latest intraocular lens formulas have used AI to enhance prediction accuracy,achieving superior postoperative refractive results compared to traditional formulas.Third,AI can be used to augment cataract surgical skill training by identifying diferent phases of cataract surgery on video and to optimize operating theater workfows by accurately predicting the duration of surgical procedures.Fourth,some AI CNN models are able to efectively predict the progression of posterior capsule opacifcation and eventual need for YAG laser capsulotomy.These advances in AI could transform cataract management and enable delivery of efcient ophthalmic services.The key challenges include ethical management of data,ensuring data security and privacy,demonstrating clinically acceptable performance,improving the generalizability of AI models across heterogeneous populations,and improving the trust of end-users.

Artifcial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology:A State‑of‑the‑Art Review

Nuclear medicine and molecular imaging plays a signifcant role in the detection and management of cardiovascular disease(CVD).With recent advancements in computer power and the availability of digital archives,artifcial intelligence(AI)is rapidly gaining traction in the feld of medical imaging,including nuclear medicine and molecular imaging.However,the complex and time-consuming workfow and interpretation involved in nuclear medicine and molecular imaging,limit their extensive utilization in clinical practice.To address this challenge,AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging.It has shown promising applications in various crucial aspects of nuclear cardiology,such as optimizing imaging protocols,facilitating data processing,aiding in CVD diagnosis,risk classifcation and prognosis.In this review paper,we will introduce the key concepts of AI and provide an overview of its current progress in the feld of nuclear cardiology.In addition,we will discuss future perspectives for AI in this domain.

Digital Twin for Integration of Design-Manufacturing-Maintenance:An Overview

Traditional design,manufacturing and maintenance are run and managed independently under their own rules and regulations in an increasingly time-and-cost inefective manner.A unifed platform for efcient and intelligent designmanufacturing-maintenance of mechanical equipment and systems is highly needed in this rapidly digitized world.In this work,the defnition of digital twin and its research progress and associated challenges in the design,manufacturing and maintenance of engineering components and equipment were thoroughly reviewed.It is indicated that digital twin concept and associated technology provide a feasible solution for the integration of design-manufacturingmaintenance as it has behaved in the entire lifecycle of products.For this aim,a framework for information-physical combination,in which a more accurate design,a defect-free manufacturing,a more intelligent maintenance,and a more advanced sensing technology,is prospected.

Artificial Intelligence-Enabled Mode-Locked Fiber Laser:A Review

Owing to their compactness,robustness,low cost,high stability,and difraction-limited beam quality,mode-locked fber lasers play an indispensable role in micro/nanomanufacturing,precision metrology,laser spectroscopy,LiDAR,biomedi-cal imaging,optical communication,and soliton physics.Mode-locked fber lasers are a highly complex nonlinear optical system,and understanding the underlying physical mechanisms or the fexible manipulation of ultrafast laser output is chal-lenging.The traditional research paradigm often relies on known physical models,sophisticated numerical calculations,and exploratory experimental attempts.However,when dealing with several complex issues,these traditional approaches often face limitations and struggles in fnding efective solutions.As an emerging data-driven analysis and processing technology,artifcial intelligence(AI)has brought new insights into the development of mode-locked fber lasers.This review highlights the areas where AI exhibits potential in accelerating the development of mode-locked fber lasers,including nonlinear dynamics prediction,ultrashort pulse characterization,inverse design,and automatic control of mode-locked fber lasers.Furthermore,the challenges and potential future development are discussed.

Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods

Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in practice.The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological factors.An enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new methodology.The findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability.This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

DARTS-Drone and Artificial Intelligence Reconsolidated Technological Solution for Increasing the Oil and Gas Pipeline Resilience

The need for safe operation and effective maintenance of pipelines grows as oil and gas demand rises.Thereby,it is increasingly imperative to monitor and inspect the pipeline system,detect causes contributing to developing pipeline damage,and perform preventive maintenance in a timely manner.Currently,pipeline inspection is performed at pre-determined intervals of several months,which is not sufficiently robust in terms of timeliness.This research proposes a drone and artificial intelligence reconsolidated technological solution(DARTS) by integrating drone technology and deep learning technique.This solution is aimed to detect the targeted potential root problems-pipes out of alignment and deterioration of pipe support system-that can cause critical pipeline failures and predict the progress of the detected problems by collecting and analyzing image data periodically.The test results show that DARTS can be effectively used to support decision making for preventive pipeline maintenance to increase pipeline system s afety and resilience.