Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

Santoro, Miriam; Strolin, Silvia; Paolani, Giulia; Gala, Giuseppe Della; Bartoloni, Alessandro; Giacometti, Cinzia; Ammendolia, I.; Morganti, Alessio Giuseppe; Strigari, Lidia

doi:10.3390/app12073223

Cited by 23 publications

(8 citation statements)

References 140 publications

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“…In recent years, advances in artificial intelligence (AI), predominantly in deep learning (DL), have rapidly improved automated contouring for RT applications, particularly for routine organs-at-risk. 1 , 2 , 3 Despite research efforts actively promoting its broader acceptance, clinical adoption of auto-contouring is not yet standard practice.…”

Section: Introductionmentioning

confidence: 99%

Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

Wahid,

Cardenas,

Marquez

et al. 2024

Advances in Radiation Oncology

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Section: Introductionmentioning

confidence: 99%

Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

Wahid,

Cardenas,

Marquez

et al. 2024

Advances in Radiation Oncology

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“…Given the differences in application, reviews of simulation training for general robotics, such as [47], focus on the use of RGB(-D) imaging, which generally does not apply in the context of MIS. Further, although previous reviews include recent advances in MIS [48][49][50][51][52][53][54][55][56][57][58][59], robotic-assisted MIS [55,[60][61][62][63], machine learning in surgical interventions [34,35,[64][65][66][67][68][69][70][71][72], or surgical simulation for human training purposes [73][74][75][76], in silico training specifically for intelligent MIS systems remains an emerging area deserving of an introduction. We focus this review on frameworks and successful applications in three imaging modalities which have received the bulk of researchers' attention, namely endoscopy, ultrasound (US), and x-ray.…”

Section: Introductionmentioning

confidence: 99%

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

et al. 2023

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To mitigate the challenges of operating through narrow incisions under image guidance, there is a desire to develop intelligent systems that assist decision making and spatial reasoning in minimally invasive surgery (MIS). In this context, machine learning-based systems for interventional image analysis are receiving considerable attention because of their flexibility and the opportunity to provide immediate, informative feedback to clinicians. It is further believed that learning-based image analysis may eventually form the foundation for semior fully automated delivery of surgical treatments. A significant bottleneck in developing such systems is the availability of annotated images with sufficient variability to train generalizable models, particularly the most recently favored deep convolutional neural networks or transformer architectures. A popular alternative to acquiring and manually annotating data from the clinical practice is the simulation of these data from human-based models. Simulation has many advantages, including the avoidance of ethical issues, precisely controlled environments, and the scalability of data collection. Here, we survey recent work that relies on in silico training of learning-based MIS systems, in which data are generated via computational simulation. For each imaging modality, we review available simulation tools in terms of compute requirements, image quality, and usability, as well as their applications for training intelligent systems. We further discuss open challenges for simulation-based development of MIS systems, such as the need for integrated imaging and physical modeling for non-optical modalities, as well as generative patient models not dependent on underlying CT, MRI, or other patient data. In conclusion, as the capabilities of in silico training mature, with respect to sim-to-real transfer, computational efficiency, and degree of control, they are contributing toward the next generation of intelligent surgical systems.

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“…Automation-based solutions are spreading in several medical sectors, including radiotherapy (RT), finding applications in the entire workflow (1).…”

Section: Introductionmentioning

confidence: 99%

How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images

et al. 2023

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BackgroundA CE- and FDA-approved cloud-based Deep learning (DL)-tool for automatic organs at risk (OARs) and clinical target volumes segmentation on computer tomography images is available. Before its implementation in the clinical practice, an independent external validation was conducted.MethodsAt least a senior and two in training Radiation Oncologists (ROs) manually contoured the volumes of interest (VOIs) for 6 tumoral sites. The auto-segmented contours were retrieved from the DL-tool and, if needed, manually corrected by ROs. The level of ROs satisfaction and the duration of contouring were registered. Relative volume differences, similarity indices, satisfactory grades, and time saved were analyzed using a semi-automatic tool.ResultsSeven thousand seven hundred sixty-five VOIs were delineated on the CT images of 111 representative patients. The median (range) time for manual VOIs delineation, DL-based segmentation, and subsequent manual corrections were 25.0 (8.0-115.0), 2.3 (1.2-8) and 10.0 minutes (0.3-46.3), respectively. The overall time for VOIs retrieving and modification was statistically significantly lower than for manual contouring (p<0.001). The DL-tool was generally appreciated by ROs, with 44% of vote 4 (well done) and 43% of vote 5 (very well done), correlated with the saved time (p<0.001). The relative volume differences and similarity indexes suggested a better inter-agreement of manually adjusted DL-based VOIs than manually segmented ones.ConclusionsThe application of the DL-tool resulted satisfactory, especially in complex delineation cases, improving the ROs inter-agreement of delineated VOIs and saving time.

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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

Cited by 23 publications

References 140 publications

Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images

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