PowerPoint Presentation

Faculty of Applied Health science. Artificial intelligence in radiotherapy.

Under supervision of :. Dr/ Muhammad yasser Alsedfy lecturer of medical Radiology Sciences and Biophysics T.A/ Lamia Al-Arabi Teaching assistant of radiology medical imaging.

Artificial intelligence.

Artificial Intelligence. The idea of artificial intelligence (AI) is believed to have originated from the idea of robots. The idea is becoming increasingly prominent with the growing use of biosynthetic machines in labor. AI can be defined as a machine's ability to imitate human.

Radiotherapy. Radiotherapy is an important component of cancer treatment, and it is estimated that almost 50% of all cancer patients receive radiotherapy during their course of illness. Radiotherapy can be classified into seven sections: imaging, treatment planning (TP) simulation, radiotherapy accessories, radiation delivery, radiotherapy verification, and patient monitoring..

The imaging process is the first step, where physicians diagnose the patient for a tumor. If a tumor is present, important information related to the tumor is collected for later use. The imaging process provides the physician with the estimated tumor volume, shape, location, surrounding organs that are at risk, and other useful information, which are very important for radiation dose delivery.

Clinical Applications & Performance". Brain Tumors (Glioblastoma): reducing manual variability.· Lung, Pancreatic, Spleen Tumors: · Whole-Body Metastasis Detection: Multi-Modal Segmentation: AUSAM handles CT, MRI, histopathology with DSCs > 90%.

Future Trends & Challenges. Emerging Trends: Multimodal AI (integrating radiology, pathology, and genomics). Explainable AI (XAI) using maps and saliency overlays. Uncertainty estimation to build clinician trust. Human-AI collaboration with interactive refinement tools.· Challenges: Data heterogeneity (domain shift). Interpretability and regulatory hurdles. Need for large, annotated datasets..

Diagnosis Multimodal neuroimaging A1 model Model Feature fusion Prognostic prediction Predicting prog P red icting treatment respo nse CD CD p red icting clincal characteristics Data availability Class imbalance Challenges Data heterogeneity Algorithmic bias Stratification Model Interpretability Data privacy.

Radiotherapy Chain. Radiotherapy is a cancer treatment using ionizing radiation to control or kill cancer cells. The whole treatment process can be described by the radiotherapy chain.

1.diagnosis and prescription. When a patient is sent to the cancer center, various clinical tests are carried out for diagnosis of the disease. These tests include medical imaging and laboratory tests, helping the radiation oncologist to find out the stage and grade of the tumor, and whether the cancer has spread to other organs..

2.patient position. simply refers to the body position of the patient during a radiotherapy session. The idea is to place the patient in a stable and comfortable position to ensure that the radiation is directed to exactly the same area in every session. This helps increase the accuracy of the treatment and reduces possible side effects..

3.immobilization and simulation. 1.immobilization device may be used to increase the positional accuracy. 2.simulation and planning. The treatment position of the patient is set up by the radiotherapists,. A computed tomography (CT) scan is performed by a CT-simulator to acquire the 3D-anatomy information of the patient..

4.target and organ contouring. precisely defines the location of the tumor so that it doesn’t affect the surrounding healthy tissue..

5.Radiation treatment planning. the process through which we plan how radiation is delivered to the patient. It includes dose calculation and directing the radiation to the target area using specialized software and advanced imaging techniques. This ensures that the treatment is both.".

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6.setup verification. done using the Cone Beam CT and portable imaging. This ensures that the patient’s position is exactly the same as during the simulation, and that the radiation beam directly targets the planned area.use the Cone Beam CT. Second, I make a simple comparison with the planning CT image. Third, if I find any differences, I adjust them right away to ensure everything is accurate..

7.Treatment Delivery and the Complication or Patient Follow-Up.

Applications of AI and Machine Learning in Radiotherapy.

In traditional radiotherapy planning, physicians rely on their experience to define target areas and radiation doses. AI and machine learning can automatically detect organs and target volumes, optimize dose distribution, reduce planning time.

1.Treatment Plan Evaluation. It is the process of choosing the best radiotherapy plan from several possible plans. Experts compare radiation dose, dose distribution, and treatment technique, while AI helps analyze plans quickly and select the most suitable one..

2. Treatment Plan QA (Quality Assurance). QA ensures that the radiotherapy treatment plan is correct and free from errors. It includes checking dose calculations and treatment parameters before delivering the treatment to ensure patient safety..

3. IoT in Radiotherapy. IoT means connecting medical devices through a network to share data. It helps improve communication between devices, reduce human errors, and manage treatment data more efficiently..

4. AI + IoT in Cone-Beam CT (CBCT). Combining AI with IoT in CBCT helps analyze images quickly and detect tumor position accurately. It can also adjust the treatment plan if the patient’s position changes, leading to safer and more precise radiotherapy..

Machine Learning for DDI. Machine learning predicts the Dose Delivery Index (DDI) by analyzing data and selecting the best model to improve treatment planning..

IMRT & MLC. IMRT delivers radiation with precise intensity for irregular tumors, while MLC shapes the radiation beam to protect healthy tissues..

Lets define The chatbot. A chatbot is a computer program designed To Simulate human Conversation through Text or voice interaction over the internet or applications. Role of chatbots in healthcare and Medical education :- the first Point is The role of Chat bot in health Care.

CHAT BOT IN DIGITIZED HEALTHCARE.

The main benefits :-. 1. Provide Medical information 2. Assisst health assessment 3. Support Communication between Patients and doctors. 4 .Reduce health care Costs and work load..

A doctor using a computer Description automatically generated.

the Second point The role of chatbots in medical education :-.

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Conclusion:. Artificial Intelligence is transforming radiotherapy by enhancing precision, efficiency, and patient care. While challenges like skill gaps, data privacy, and standardization remain, ongoing advancements in data management, analytics, and user-friendly tools promise a smarter, safer, and more effective future for radiation oncology..

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