Accurate patient alignment without unnecessary imaging using patient-specific 3D CT images synthesized from 2D kV images DOI Creative Commons
Yuzhen Ding, Jason Holmes, Hongying Feng

et al.

Communications Medicine, Journal Year: 2024, Volume and Issue: 4(1)

Published: Nov. 21, 2024

In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging (OBI) is unavailable. However, tumor visibility constrained due to the projection of patient's anatomy onto a plane, potentially leading substantial setup errors. treatment room with 3D-OBI such as cone beam CT (CBCT), field view (FOV) CBCT limited unnecessarily high dose. A solution this dilemma reconstruct 3D from obtained at position. We propose dual-models framework built hierarchical ViT blocks. Unlike proof-of-concept approach, our considers acquired by devices in solo input and can synthesize accurate, full-size within milliseconds. demonstrate feasibility proposed approach on 10 patients head neck (H&N) cancer using image quality (MAE: < 45HU), dosimetric accuracy (Gamma passing rate ((2%/2 mm/10%): > 97%) position uncertainty (shift error: 0.4 mm). The generate accurate faithfully mirroring effectively, thus substantially improving accuracy, keeping dose minimal, maintaining veracity. Effective guidance critical precise alignment, tracking, delivery radiation therapy protect organs that should not be irradiated. high-quality usually only provided following detailed large amount radiation. computational method full size required X-Ray images. demonstrated its utility data people cancer. Our existing machines improve hence ensure more patients. Ding et al. deep learning-based model fast reconstruction given (X-Ray) inputs. experimental results analysis indicate robust minimum

Language: Английский

Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant DOI Creative Commons

Mingze Yuan,

Peng Bao, Jiajia Yuan

et al.

Medicine Plus, Journal Year: 2024, Volume and Issue: 1(2), P. 100030 - 100030

Published: May 17, 2024

With the rapid development of artificial intelligence, large language models (LLMs) have shown promising capabilities in mimicking human-level comprehension and reasoning. This has sparked significant interest applying LLMs to enhance various aspects healthcare, ranging from medical education clinical decision support. However, medicine involves multifaceted data modalities nuanced reasoning skills, presenting challenges for integrating LLMs. review introduces fundamental applications general-purpose specialized LLMs, demonstrating their utilities knowledge retrieval, research support, workflow automation, diagnostic assistance. Recognizing inherent multimodality medicine, emphasizes multimodal discusses ability process diverse types like imaging electronic health records augment accuracy. To address LLMs' limitations regarding personalization complex reasoning, further explores emerging LLM-powered autonomous agents healthcare. Moreover, it summarizes evaluation methodologies assessing reliability safety contexts. transformative potential medicine; however, there is a pivotal need continuous optimizations ethical oversight before these can be effectively integrated into practice.

Language: Английский

Citations

17
LLM-driven multimodal target volume contouring in radiation oncology DOI Creative Commons
Yujin Oh, Sang Joon Park, Hwa Kyung Byun

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 24, 2024

Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates utilization of both image and text-based clinical information.Inspired by recent advancement large language models (LLMs) that can facilitate integration textural information images, here we present an LLM-driven multimodal artificial intelligence (AI), namely LLMSeg, utilizes applicable to task 3-dimensional context-aware target delineation oncology.We validate our proposed LLMSeg within context breast cancer radiotherapy using external validation data-insufficient environments, which attributes highly conducive real-world applications.We demonstrate exhibits markedly improved performance compared conventional unimodal AI models, particularly exhibiting robust generalization data-efficiency.

Language: Английский

Citations

13
Preparing for Artificial General Intelligence (AGI) in Health Professions Education: AMEE Guide No. 172 DOI Creative Commons
Ken Masters, Anne Herrmann‐Werner, Teresa Festl‐Wietek

et al.

Medical Teacher, Journal Year: 2024, Volume and Issue: 46(10), P. 1258 - 1271

Published: Aug. 8, 2024

Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is

Language: Английский

Citations

11
Understanding LLMs: A comprehensive overview from training to inference DOI
Yiheng Liu,

Hao He,

Tianle Han

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 129190 - 129190

Published: Dec. 1, 2024

Language: Английский

Citations

8
Research and application progress of radiomics in neurodegenerative diseases DOI Creative Commons
Junbang Feng, Ying Huang,

X. Zhang

et al.

Meta-Radiology, Journal Year: 2024, Volume and Issue: 2(1), P. 100068 - 100068

Published: Feb. 22, 2024

Neurodegenerative diseases refer to degenerative of the nervous system caused by neuronal degeneration and apoptosis. Usually, onset disease is insidious, progression slow, which can last for several years decades. Clinical symptoms only appear in later stages pathological changes when degree nerve cell loss reaches or exceeds a certain threshold. Traditional electrophysiological medical imaging techniques lack valuable indicators markers. Therefore, early diagnosis differentiation are very difficult. Radiomics new technology merged recent years, extract large number invisible features from raw image data with high throughput, quantitatively analyze physiological changes. It demonstrates important potential value diagnosis, grading, prognosis evaluation NDs. This review provides an overview research progress radiomics neurodegenerative diseases, emphasizing process principles its application classification, prediction these diseases. helps deepen understanding promote treatment clinical practice.

Language: Английский

Citations

6
Artificial general intelligence for the upstream geoenergy industry: a review DOI Creative Commons
Jimmy Xuekai Li, Tiancheng Zhang, Yiran Zhu

et al.

Gas Science and Engineering, Journal Year: 2024, Volume and Issue: 131, P. 205469 - 205469

Published: Oct. 10, 2024

Language: Английский

Citations

4
Predictors of Radiation Resistance and Novel Radiation Sensitizers in Head and Neck Cancers: Advancing Radiotherapy Efficacy DOI
Aastha Sobti, Heath D. Skinner, Christopher Wilke

et al.

Seminars in Radiation Oncology, Journal Year: 2025, Volume and Issue: 35(2), P. 224 - 242

Published: March 14, 2025

Language: Английский

Citations

0
Critical review of patient outcome study in head and neck cancer radiotherapy DOI Creative Commons
Jingyuan Chen,

Yunze Yang,

Chenbin Liu

et al.

Meta-Radiology, Journal Year: 2025, Volume and Issue: unknown, P. 100151 - 100151

Published: April 1, 2025

Language: Английский

Citations

0
Artificial general intelligence for neurosurgery and medicine DOI
Partha Pratim Ray

Journal of Clinical Neuroscience, Journal Year: 2024, Volume and Issue: 125, P. 104 - 105

Published: May 17, 2024

Language: Английский

Citations

1
Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review DOI Creative Commons
Duvern Ramiah, Daniel Mmereki

Clinical Medicine Insights Oncology, Journal Year: 2024, Volume and Issue: 18

Published: Jan. 1, 2024

The promise of novel technologies to increase access radiotherapy in low- and middle-income countries (LMICs) is crucial, given that the cost equipping new centres or upgrading existing machinery remains a major obstacle expanding cancer treatment. study aims provide thorough analysis overview how technological advancement may revolutionize (RT) improve level care provided patients. A comprehensive literature review following some steps systematic (SLR) was performed using Web Science (WoS), PubMed, Scopus databases. findings are classified into different technologies. Artificial intelligence (AI), knowledge-based planning, remote radiotherapy, scripting all ways patient flow across radiation oncology, including initial consultation, treatment delivery, verification, follow-up. This found these delineation organ at risks (OARs) considerably reduce waiting times when compared with conventional planning RT. In this review, AI, reduced improved at-risk RT planning. combination lower patients’ risk disease progression due workload, quality therapy, individualized Efficiency tools, such as application scripting, urgently needed OAR accuracy traditional methods. study’s contribution present potential optimize process, thereby improving resource utilization. be extended future include digital integration technology’s impact on safety, outcomes, risk. Therefore, research more efficient tools pioneers development implementation high-precision for

Language: Английский

Citations

1