Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation
Abstract This research introduces a novel quantum-enhanced intelligent system tailored for personalized adaptive radiotherapy dose estimation. The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Mont...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05673-y |
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| _version_ | 1849434193160830976 |
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| author | Radhey Lal Rajiv Kumar Singh Dinesh Kumar Nishad Saifullah Khalid |
| author_facet | Radhey Lal Rajiv Kumar Singh Dinesh Kumar Nishad Saifullah Khalid |
| author_sort | Radhey Lal |
| collection | DOAJ |
| description | Abstract This research introduces a novel quantum-enhanced intelligent system tailored for personalized adaptive radiotherapy dose estimation. The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Monte Carlo simulations. Quantum-enhanced Monte Carlo simulations, employing algorithms such as Harrow-Hassidim-Lloyd (HHL) and Variational Quantum Eigensolver (VQE), achieve computational speedups of 8–15 times compared to classical methods while maintaining high accuracy. The deep learning architecture leverages convolutional and recurrent neural networks to capture complex anatomical and dosimetric patterns. Validation on simulated datasets demonstrates a 50–70% reduction in mean absolute error and 2–3% improvements in gamma index metrics compared to conventional approaches. Dose-volume histogram analysis further highlights enhanced Dice coefficients and reduced Hausdorff distances. These advancements underscore the potential for precise, efficient, and clinically relevant dose estimations, paving the way for improved outcomes in personalized adaptive radiotherapy. |
| format | Article |
| id | doaj-art-664ed8050efc4adebfa37e97de30a2d8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-664ed8050efc4adebfa37e97de30a2d82025-08-20T03:26:44ZengNature PortfolioScientific Reports2045-23222025-06-0115112610.1038/s41598-025-05673-yQuantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimationRadhey Lal0Rajiv Kumar Singh1Dinesh Kumar Nishad2Saifullah Khalid3Dr. APJ Abdul, Kalam Technical University LucknowInstitute of Engineering and TechnologyDepartment of Electrical Engineering, Dr. Shakuntala Misra National Rehabilitation UniversityIBM Multi Activities Co. Ltd. KhartoumAbstract This research introduces a novel quantum-enhanced intelligent system tailored for personalized adaptive radiotherapy dose estimation. The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Monte Carlo simulations. Quantum-enhanced Monte Carlo simulations, employing algorithms such as Harrow-Hassidim-Lloyd (HHL) and Variational Quantum Eigensolver (VQE), achieve computational speedups of 8–15 times compared to classical methods while maintaining high accuracy. The deep learning architecture leverages convolutional and recurrent neural networks to capture complex anatomical and dosimetric patterns. Validation on simulated datasets demonstrates a 50–70% reduction in mean absolute error and 2–3% improvements in gamma index metrics compared to conventional approaches. Dose-volume histogram analysis further highlights enhanced Dice coefficients and reduced Hausdorff distances. These advancements underscore the potential for precise, efficient, and clinically relevant dose estimations, paving the way for improved outcomes in personalized adaptive radiotherapy.https://doi.org/10.1038/s41598-025-05673-yQuantum computingArtificial intelligenceRadiation therapyMonte Carlo simulationDeep learningDose estimation |
| spellingShingle | Radhey Lal Rajiv Kumar Singh Dinesh Kumar Nishad Saifullah Khalid Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation Scientific Reports Quantum computing Artificial intelligence Radiation therapy Monte Carlo simulation Deep learning Dose estimation |
| title | Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation |
| title_full | Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation |
| title_fullStr | Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation |
| title_full_unstemmed | Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation |
| title_short | Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation |
| title_sort | quantum enhanced intelligent system for personalized adaptive radiotherapy dose estimation |
| topic | Quantum computing Artificial intelligence Radiation therapy Monte Carlo simulation Deep learning Dose estimation |
| url | https://doi.org/10.1038/s41598-025-05673-y |
| work_keys_str_mv | AT radheylal quantumenhancedintelligentsystemforpersonalizedadaptiveradiotherapydoseestimation AT rajivkumarsingh quantumenhancedintelligentsystemforpersonalizedadaptiveradiotherapydoseestimation AT dineshkumarnishad quantumenhancedintelligentsystemforpersonalizedadaptiveradiotherapydoseestimation AT saifullahkhalid quantumenhancedintelligentsystemforpersonalizedadaptiveradiotherapydoseestimation |