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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works
The Optimisation of Stochastic Grammars to Enable Cost-Effective Probabilistic Structural Testing
The effectiveness of probabilistic structural testing depends on the characteristics of the probability distribution from which test inputs are sampled at random. Metaheuristic search has been shown to be a practical method of optimis- ing the characteristics of such distributions. However, the applicability of the existing search-based algorithm is lim- ited by the requirement that the software’s inputs must be a fixed number of numeric values. In this paper we relax this limitation by means of a new representation for the probability distribution. The repre- sentation is based on stochastic context-free grammars but incorporates two novel extensions: conditional production weights and the aggregation of terminal symbols represent- ing numeric values. We demonstrate that an algorithm which combines the new representation with hill-climbing search is able to effi- ciently derive probability distributions suitable for testing software with structurally-complex input domains
The Agile Alert System For Gamma-Ray Transients
In recent years, a new generation of space missions offered great
opportunities of discovery in high-energy astrophysics. In this article we
focus on the scientific operations of the Gamma-Ray Imaging Detector (GRID)
onboard the AGILE space mission. The AGILE-GRID, sensitive in the energy range
of 30 MeV-30 GeV, has detected many gamma-ray transients of galactic and
extragalactic origins. This work presents the AGILE innovative approach to fast
gamma-ray transient detection, which is a challenging task and a crucial part
of the AGILE scientific program. The goals are to describe: (1) the AGILE
Gamma-Ray Alert System, (2) a new algorithm for blind search identification of
transients within a short processing time, (3) the AGILE procedure for
gamma-ray transient alert management, and (4) the likelihood of ratio tests
that are necessary to evaluate the post-trial statistical significance of the
results. Special algorithms and an optimized sequence of tasks are necessary to
reach our goal. Data are automatically analyzed at every orbital downlink by an
alert pipeline operating on different timescales. As proper flux thresholds are
exceeded, alerts are automatically generated and sent as SMS messages to
cellular telephones, e-mails, and push notifications of an application for
smartphones and tablets. These alerts are crosschecked with the results of two
pipelines, and a manual analysis is performed. Being a small scientific-class
mission, AGILE is characterized by optimization of both scientific analysis and
ground-segment resources. The system is capable of generating alerts within two
to three hours of a data downlink, an unprecedented reaction time in gamma-ray
astrophysics.Comment: 34 pages, 9 figures, 5 table
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