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2024 MAR 2 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Research findings on Oncology - Head and Neck Cancer are discussed in [...]

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70–74 Gy/28–37 fr) at our hospital were used (n= 55 for training and n= 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans—i.e. preDose and deliDose—with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04–6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00–15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P= 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer.

2022 DEC 10 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Current study results on Obesity, Fitness and Wellness have been published. According [...]

2022 NOV 12 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Investigators publish new report on Machine Learning. According to news originating from [...]

2022 OCT 1 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Investigators publish new report on Clinical Oncology. According to news reporting originating [...]

* Hierarchical planning has attracted renewed interest in the last few years. Consequently, the time was right to establish a workshop devoted entirely to hierarchical planning--an insight shared by many [...]

2022 JAN 29 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Current study results on brain cancer have been published. According to news [...]

Preoperative CT-based planning is established in shoulder arthroplasty surgery. Automated planning software has become available to assist the surgeon and may increase reliability and efficiency. This study aims to evaluate the reliability of an automated 3-dimensional (3D) planning software package (Blueprint™ v2.1.5, Wright Medical Ltd) in the assessment of the arthritic shoulder against manual multiplanar measurement (MM).

Offshore oil and gas industry has a strong incentive to improve its traditional operations and move towards more remote controlled and automated installations. This allows for improved efficiency, reduced cost and improved quality, and safety by removing personnel out of harm's way. The use of Unmanned Ground Vehicles (UGVs) in these upcoming platforms, is relevant for Inspection and Maintenance (I&M) operations. Traditionally, UGVs are used only for pre-defined tasks and have no capabilities for replanning, if a new task is required or any unexpected event occurs. This paper presents a novel concept for I&M operations using automated planning for UGVs. The automated planner is based on a temporal planning algorithm, and considers actions related to, for example, visiting a specific waypoint, inspect a sensor or manipulate an actuator. Also, the proposed system allows to perform replanning in case of any specific location needs to be revisited or a path is blocked. In addition, we couple the mission planner with a UGV guidance, navigation and control system, which has path planning, path following and control capabilities. To assess the performance of the proposed system, an use case for I&M operations on board of an oil and gas platform was simulated and promising results were obtained.

2021 MAY 8 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- A new study on Drugs and Therapies - Radiotherapy is now available. [...]

2020 MAR 7 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- A patent by the inventors Hedblom, Anders (Linkoping, SE); Scheiderbauer, Rolf (Linkoping, [...]

Personalized sequencing and recommendation of pedagogical actions in virtual learning environments are relevant aspects in promoting an effective learning process with computer-aided support. Hence, this work investigates the use of automated planning to sequence these actions according to student profiles. Actions are modeled to correspond to the cognitive process described by Bloom’s Taxonomy, and the student profile is set using the Revised Approaches to Studying Inventory. Both models share theoretical foundations linked to the cognitive process, and the mapping of these two theories is one of the contributions merged into this study. In planning, through use of a genetic algorithm and the problem formulation as an optimization problem, one can correctly manage the search for good solutions, as demonstrated in this work. Through use of Digital Bloom’s Taxonomy, one arrives at a recommend set of actions. Experiments were performed using 41 students. The results were promising and demonstrate the viability of the proposal.

2019 DEC 7 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- A new study on Physics - Medical Physics is now available. According [...]

Heuristic search provides an efficient way to automatically explore the state space in planning, while Control Knowledge (CK) also has the potential to significantly increase the performance of planners. Currently, most of the state-of-the-art planners primarily rely on sophisticated heuristic mechanisms. However, these planners fail to scale up and to provide (high-quality) solutions in a range of problems. The objective of this paper is to incorporate CK with heuristic search in order to leverage the advantages of both, thus leading planners to achieve much higher efficiency. To achieve this, we introduce a novel CK which is specified by a variant of Linear Temporal Logic (LTL), referred to as LTL P. We propose an encoding methodology that translates LTL P into standard planning models. Consequently, we can directly use existing heuristic planners to solve the augmented problem, and avoid tailoring the planners in order to deal with CK implicitly. The novelty of this approach lies in that we define a useful CK LTL P with a concise encoding methodology, that can significantly improve the efficiency of heuristic search. In this paper, the encoding process is formally presented, and theoretical results on the complexity and soundness of the encoding are strictly proved. We find that appropriate CK is a good complement to heuristic search, and is capable of making hard problems easy to solve. Experiments demonstrate that our approach shows highly competitive results versus heuristic search and other CK-based techniques on many intractable benchmark problems, benefiting in improving the coverage and quality of plans. [ABSTRACT FROM AUTHOR]

This patent application discusses a new technique for tracking subject motion during medical imaging procedures. Instead of using physical fiducial markers, the invention proposes the use of virtual fiducial markers generated by an image marking neural network. These virtual markers can be used in existing algorithms and techniques, and they offer advantages such as avoiding the need for physical markers and providing more flexibility in dealing with different types of motion. The patent application describes the components of the medical system and the process of generating and using the virtual fiducial markers. [Extracted from the article]

The design of assembly lines is a demanding task, whose outcome is highly dependent on the production engineers’ knowledge and experience. Additionally, it is very time-consuming when performed manually. For this reason, many approaches exist to automate the required planning phases with their sub-tasks. This paper presents an integrated modeling approach using AutomationML as seamless data exchange format for the sequentially required tasks to design and configure an assembly line against multiple user-defined criteria. In its simple form, the design of an assembly line requires the assignment of processes to a set of resources and workstations (conceptual design), and decisions about the spatial arrangement of workstations and their connection through a conveyor or AGV system (physical layout). After every planning phase the model is extended and enriched with additional information required for the next phase and its automated tools.

ABSTRACTToday’s fast-paced business world poses many challenges to companies. Amongst them is the necessity to quickly react to needs for change due to shifts in the competitive environment. Hence, achieving a high flexibility of business processes while maintaining their quality has become crucial. We address this issue by proposing an automated planning approach capable of adapting existing process models to upcoming needs for change. This means that the needs for change have not yet been implemented and the adapted process models have not yet been realised. The approach constructs adapted process models, which are complete and correct. We evaluate our approach by means of mathematical proofs, a prototypical implementation and an application in a real-world situation. Further, by means of a computational complexity analysis and a simulation experiment, its runtime is benchmarked against planning process models from scratch.

2018 AUG 11 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Research findings on Oncology - Oropharyngeal Cancer are discussed in a new [...]

The Seventeenth International Conference on Automated Planning and Scheduling (ICAPS-07) was held in Providence, Rhode Island, in September 2007. It covered the latest theoretical and practical advances in planning and scheduling. The conference was collocated with the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP-07). The program consisted of tutorials, workshops, system demonstrations, a doctoral consortium, and three days of technical presentations mostly in parallel sessions. ICAPS-07 also hosted the second edition of the International Competition on Knowledge Engineering for Planning and Scheduling. This report describes the conference in more detail., The International Conference on Automated Planning and Scheduling (ICAPS) is well established as the premier forum for researchers and practitioners in the area. The annual conference series was formed in [...]

Automated planning technology has become mature enough to be useful in applications that range from game playing to control of space vehicles. In this article, Dana Nau discusses where automated-planning research has been, where it is likely to go, where he thinks it should go, and some major challenges in getting there. The article is an updated version of Nau's invited talk at AAAI-05 in Pittsburgh, Pennsylvania., In ordinary English, plans can be many different kinds of things, such as project plans, pension plans, urban plans, and floor plans. In automated-planning research, the word refers specifically to [...]

Business Process Management (BPM) is a central element of today’s organizations. Over the years, its main focus has been the support of business processes (BPs) in highly controlled domains. However—in the current era of Big Data and Internet-of-Things—several real-world domains are becoming cyber-physical (e.g., consider the shift from traditional manufacturing to Industry 4.0), characterized by ever-changing requirements, unpredictable environments and increasing amounts of data and events that influence the enactment of BPs. In such unconstrained settings, BPM professionals lack the needed knowledge to model all possible BP variants/contingencies at the outset. Consequently, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents). In this context, automated planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviors in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for solving concrete problems in the BPM field that were previously tackled with hard-coded solutions. To this aim, we first propose a methodology that shows how a researcher/practitioner should approach the task of encoding a concrete problem as an appropriate planning problem. Then, we discuss the required steps to integrate the planning technology in BPM environments. Finally, we show some concrete examples of the successful application of planning techniques to the different stages of the BPM life cycle.

2017 DEC 2 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Current study results on Oncology - Liver Cancer have been published. According [...]

2017 JUN 10 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Investigators publish new report on Oncology - Prostate Cancer. According to news [...]

AIRLINE INDUSTRY INFORMATION-(C)1997-2017 M2 COMMUNICATIONS Quintiq, a Dassault Systèmes brand and global leader in supply chain planning and optimization (SCP&O), has been selected by Dubai Airports to plan and schedule [...]

In this article, we consider the symbiosis of two of the most broadly recognized hall. marks of intelligence: (1) planning--solving problems in which one uses beliefs about actions and their […], This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.

ABSTRACTIn many business processes, multiple actors such as different employees, departments or companies are involved. These actors need to work together and form appropriate partnerships in parts of these processes to achieve their individual goals. Hence, from a Business Process Management perspective, the actors need to cooperate. We present a conceptual foundation for multi-actor process models, which enables the consideration of individual starting points and goals as well as partnerships. Further, we incorporate the cooperation of actors in the control flow of process models by constructing explicit actions determining where in the process to form and disband partnerships. We pursue an automated planning approach due to the complexity of the required cooperation. The constructed multi-actor process models are proven to be correct and complete. We demonstrate the feasibility of our approach by an application in several real-world scenarios and its effectiveness through the assessment of a practitioner.

A recent report discusses research conducted at the University of Miami on the feasibility of using cone beam CT-based adaptive intensity modulated proton therapy (IMPT) for the treatment of head and neck cancers. The study included 20 patients who received radiotherapy and had pretreatment CT scans. Synthetic CTs were created by deforming the planning CT to the pretreatment CTs, and dose calculation accuracy was assessed using gamma analysis. The research found that adaptive IMPT resulted in better coverage of the target area, sparing of organs at risk, and lower normal tissue complication probability compared to non-adaptive IMPT. [Extracted from the article]

ABSTRACTArtificial-intelligence (AI) researchers have made very considerable advances in their theoretical knowledge of planning over the past few decades. But the impact of AI on society in the coming years will depend on how much these discoveries improve the real-world performance of automated planning, or AP, an AI subfield that seeks to create computer programs that can generate plans to achieve a particular goal. If practical applications of automated planning continue to stagnate, it could hold back all of AI, even as its other subfields continue to mature. Modest progress, meanwhile, would facilitate modest economic and military uses of artificial intelligence. And should AP experience the same kind of spectacular breakout as reinforcement learning, which is being used practically in a wide variety of fields, from robotics to finance, the peril and promise of artificial intelligence might be fully realized.

The International Conference on Automated Planning and Scheduling (ICAPS) is emerging as the premier forum for researchers and practitioners in planning and scheduling. The annual ICAPS conference series was formed […], The Fourteenth International Conference on Automated Planning and Scheduling (ICAPS-04) was held in Canada in June of 2004. It covered the latest theoretical and empirical advances in planning and scheduling. The conference program consisted of tutorials, workshops, a doctoral consortium, and three days of technical paper presentations in a single plenary track, one day of which was jointly organized with the Ninth International Conference on Principles of Knowledge Representation and Reasoning. ICAPS-04 also hosted the International Planning Competition, including a classical track and a newly formed probabilistic track. This report describes the conference in more detail.

We show how reactive synthesis and automated planning can be leveraged effectively to find nonmaximal solutions to deterministic supervisory control problems of discrete event systems. To do so, we propose efficient translations of the supervisory control problem into the reactive synthesis and planning frameworks. Notably, our translation methods capture the compositional and reactive nature of control specifications, avoiding a potential exponential explosion found in alternative translation approaches. Additionally, we report on experimental results comparing the efficacy of different tools from the three disciplines, for a particular supervisory control benchmark. [ABSTRACT FROM AUTHOR]

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. These trends are occurring in the context of concerns around environmental issues of poor air quality and transport related carbon dioxide emissions. One out of several ways to help meet these challenges is in the intelligent routingof road traffic through congested urban areas. Our goal is to show the feasibility of using automated planning to perform this routing, taking into account a knowledge of vehicle types, vehicle emissions, route maps, air quality zones, etc. Specifically focusing on air quality concerns, in this paper we investigate the problem where the goals are to minimise overall vehicle delay while utilising network capacity fully, and respecting air quality limits. We introduce an automated planning approach for the routing of traffic to address these areas. The approach has been evaluated on micro-simulation models that use real-world data supplied by our industrial partner. Results show the feasibility of using AI planning technology to deliver efficient routes for vehicles that avoid the breaking of air quality limits, and that balance traffic flow through the network.

This study describes the use of CT images in atlas-based automated planning methods for acetabular cup implants in total hip arthroplasty (THA). The objective of this study is to develop an automated cup planning method considering the statistical distribution of the residual thickness. From a number of past THA planning datasets, we construct two statistical atlases that represent the surgeon’s expertise. The first atlas is a pelvis-cup merged statistical shape model (PC-SSM), which encodes global spatial relationships between the patient anatomy and implant. The other is a statistical residual thickness map (SRTM) of the implant surface, which encodes local spatial constraints of the anatomy and implant. In addition to PC-SSM and SRTM, we utilized the minimum thickness as a threshold constraint to prevent penetration. The proposed method was applied to the pelvis shapes segmented from CT images of 37 datasets of osteoarthritis patients. Automated planning results with manual segmentation were compared to the plans prepared by an experienced surgeon. There was no significant difference in the average cup size error between the two methods (1.1 and 1.2 mm, respectively). The average positional error obtained by the proposed method, which integrates the two atlases, was significantly smaller (3.2 mm) than the previous method, which uses single atlas (3.9 mm). In the proposed method with automated segmentation, the size error of the proposed method for automated segmentation was comparable (1.1 mm) to that for manual segmentation (1.1 mm). The average positional error was significantly worse (4.2 mm) than that using manual segmentation (3.2 mm). If we only consider mildly diseased cases, however, there was no significance between them (3.2 mm in automated and 2.6 mm in manual segmentation). We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon’s preference during cup planning.

The application of intelligent systems in industrial environments is still restricted, although autonomous systems based on artificial intelligence (AI) techniques have been under development for over 50 years and spreading into several areas since the last decade. Such integration goes against cultural and technical factors found on the shop floor and its project is often challenging. Classical industrial systems make use of programmable logic controllers (PLCs), which centralise control by using a simple and low-level language. Automated planning solutions use a formal language called planning domain definition language (PDDL) to address specific problems, with predefined initial and target scenarios. Such feature seems to be interesting in some cases, since the generic domain models cannot predict all possible scenarios in a domain. Therefore, the applicability of intelligent systems in industrial cases is directly linked to the development of applications that allow integrating different languages, thus ensuring the access to PLCs and analysing specific situations. The current paper presents an application that integrates automated planning solution and PLCs as well as analyses such integrations’ functionality. Preliminary results are shown by using object process control (OPC) and TCP/IP client/server approaches. A comparison with classical approaches was presented for a specific case study.

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A study conducted by researchers at Chongqing University in China aimed to improve the quality of automated plans for cervical cancer treatment. The study compared manual and automated planning methods and found that the adjusted automated plan, which modified certain parameters, effectively protected organs at risk (OARs) while maintaining plan quality. The study concluded that the adjusted plan had lower complexity compared to the manual plan. This research provides valuable insights into optimizing treatment plans for cervical cancer patients. [Extracted from the article]

This paper proposes a novel approach for automated planning of robotic MAG welding processes based on an adaptive gap model. The gap model describes the relation between welding parameters and a changing gap geometry resulting from deviations of relative part locations in a welding assembly. A matching process is performed between computer-aided design model and a measured point cloud of the welding assembly using an Iterative Closest Point algorithm to compute the actual gap geometry. Experimental validation for various gap geometries is demonstrated with an industrial robot equipped with stereo camera and welding gun indicating an increased weld quality.

Reduction is a crucial step in the surgical treatment of bone fractures. Finding an optimal path for restoring anatomical alignment is considered technically demanding because collisions as well as high forces caused by surrounding soft tissues can avoid desired reduction movements. The repetition of reduction movements leads to a trial-and-error process which causes a prolonged duration of surgery. By planning an appropriate reduction path—an optimal sequence of target-directed movements—these problems should be overcome. For this purpose, a computer-based method has been developed. Using the example of simple femoral shaft fractures, 3D models are generated out of CT images. A reposition algorithm aligns both fragments by reconstructing their broken edges. According to the criteria of a deduced planning strategy, a modified A*-algorithm searches collision-free route of minimal force from the dislocated into the computed target position. Muscular forces are considered using a musculoskeletal reduction model (OpenSimmodel), and bone collisions are detected by an appropriate method. Five femoral SYNBONE models were broken into different fracture classification types and were automatically reduced from ten randomly selected displaced positions. Highest mean translational and rotational error for achieving target alignment is $$1.2 \pm 0.9\,\hbox {mm}$$ 1.2±0.9mm and $$2.6^{\circ } \pm 2.8^{\circ }$$ 2.6∘±2.8∘ . Mean value and standard deviation of occurring forces are $$15.83 \pm 5.05\,\hbox {N}$$ 15.83±5.05N for M. tensor fasciae lataeand $$3.53 \pm 1.8\,\hbox {N}$$ 3.53±1.8N for M. semitendinosusover all trials. These pathways are precise, collision-free, required forces are minimized, and thus regarded as optimal paths. A novel method for planning reduction paths under consideration of collisions and muscular forces is introduced. The results deliver additional knowledge for an appropriate tactical reduction procedure and can provide a basis for further navigated or robotic-assisted developments.

This article from Computer Act!ve recommends several free software programs. The first program, Calibre 7.0, is an open-source ebook manager that allows users to read and manage ebooks, convert formats, edit content, and more. The second program, HandBrake 1.7, is a video converter that supports various formats and includes presets for optimizing resolution and file size. The third program, Wise Auto Shutdown 2.05, simplifies the process of scheduling automatic shutdowns, restarts, and other tasks on Windows computers. The article also provides information on AVG TuneUp for PC, a paid PC optimization tool that the author does not recommend. Additionally, the article answers reader questions about organizing items using the Joplin app, finding an alternative to Advanced SystemCare, and converting DWG files to PDF. [Extracted from the article]

STOCKHOLM -- Clinical guidelines and decision-making tools offer evidence-based data and prognostic estimates that can help streamline treatment tailoring. But physicians should not rely blindly on these applications as they [...]

Researchers at the Department of Medical Physics have developed an automated workflow for generating treatment plans for prostate radiotherapy patients using deep learning models and scripted optimization. The study aimed to streamline the time-intensive task of treatment planning by creating a 'single-click' workflow within a commercial treatment planning system. Results showed that the automatically generated treatment plans were comparable to clinical plans, demonstrating the potential for automated plan generation in radiation oncology. The research was published in Physics and Imaging in Radiation Oncology and can be accessed for free online. [Extracted from the article]

Automated planning, scheduling Company has announced the availability of Resource Manager version 2001. Designed as an add-on to the established Excel spreadsheet, Resource Manager 2001 offers a planning and scheduling [...]

Swarm automated planning algorithm is a planning method based on planning graph technology, and the algorithm which could improve the searching efficiency through importing swarm intelligence has the character of global and parallel. Yet sometimes the algorithm has the shortcoming in the local searching. For instance, maybe the quality of the best candidate planning solution would get lower after another around of searching. To enhance the ability of the local searching and the convergence acceleration, the swarm automated planning algorithm which with the local repairing operators is presented in this paper. The searching efficiency could be bettered through the pertinence repairing operators and the heuristic evaluation information to control the repairing processes.

A new report from Memorial Sloan-Kettering Cancer Center in New York City discusses the development of an automated planning framework for re-irradiation in radiation oncology. The study aimed to address the lack of dedicated tools in commercial planning systems for efficient review and planning of re-irradiation. The researchers implemented a fully-automated workflow called DART (Dose Accumulation-based Re-irradiation Tool) within the Eclipse planning system. The results showed that DART plans improved target coverage, reduced high-dose spillage, and improved plan quality compared to clinical plans. The researchers concluded that DART provides a powerful framework for more tailored re-irradiation plans, potentially improving outcomes for re-irradiation patients. [Extracted from the article]

The General Services Administration's Office of Governmentwide Policy recently amended rules for conference planning in its Federal Travel Regulation. 'This was a major, long-overdue overhaul,' said Nancy Murphy, a program […]

A study conducted at Shanghai Jiao Tong University focused on developing an automated preoperative planning algorithm for mandibular angle osteotomy based on anatomical landmarks detection. The algorithm was found to significantly improve efficiency, safety, and symmetry in preoperative planning, as well as enhance esthetics. The researchers concluded that this algorithm could benefit surgeons by improving preoperative planning for craniomaxillofacial personalized design surgeries in the future. For more information, readers can refer to the Journal of Craniofacial Surgery. [Extracted from the article]

Automated planning is a very active area of research within artificial intelligence. Broadly this discipline deals with the methods by which an agent can independently determine the sequence of actions required to successfully achieve a set of objectives. In this paper, we will present work outlining a new approach to planning based on clustering techniques, in order to group states of the world together and use the fundamental structure of the world to lift out more abstract representations. We will show that this approach can limit the combinatorial explosion of a typical planning problem in a way that is much more intuitive and reusable than has previously been possible, and outline ways that this approach can be developed further.

A study conducted by Tianjin Medical University Cancer Institute and Hospital aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment. The study enrolled 30 cervical cancer patients and compared the automated TOMO plans with manual TOMO plans. The results showed that the automated TOMO plans maintained similar target dose uniformity, improved target conformity, reduced radiation dose to organs at risk, and demonstrated better consistency in plan quality. Additionally, the automated TOMO planning method significantly improved the efficiency of treatment planning. [Extracted from the article]

The 2003 International Conference on Automated Planning and Scheduling (ICAPS-03) was held 9 to 13 June 2003 in Trento, Italy. It was chaired by Enrico Giunchiglia (University of Genova), Nicola […]