Cervical cancer remains a significant global health concern, characterized by high morbidity and mortality rates. High-dose-rate brachytherapy (HDR-BT) is a critical component of cervical cancer treatment, requiring precise and efficient treatment planning. However, the process is labor-intensive, heavily reliant on operator expertise, and prone to variability due to factors such as applicator shifts and organ filling changes. Recent advancements in artificial intelligence (AI), particularly in medical image processing, offer significant potential for automating and standardizing treatment planning in HDR-BT. This review examines the progress and challenge of AI applications in HDR-BT treatment planning, focusing on automatic segmentation, applicator reconstruction, dose calculation, and plan optimization. By addressing current limitations and exploring future directions, this paper aims to guide the integration of AI into clinical practice, ultimately improving treatment accuracy, reducing preparation time, and enhancing patient outcomes.

Purpose: A lightweight deep learning network SMA-Net was proposed to intelligently segment the skeletal muscle of the third lumbar (L3) level in patients with cervical cancer radiotherapy, and the segmentation performance of the network was evaluated. Methods and Materials: A total of 160 eligible patients with cervical cancer admitted to the oncology department of our hospital from September 2021 to June 2024 were randomly divided into training set (N = 112), validation set (N = 16) and test set (N = 32) according to 7 : 1 : 2. The lightweight Mamba architecture is introduced into the UNet network, and the SAB and CAB attention mechanisms are introduced on the skip connection. The attention mechanism is used to suppress the irrelevant information in the image and highlight the important local features. The trained network is geometrically evaluated on the test set for segmentation performance, comparison of manual segmentation and predicted skeletal muscle area (SMA). Compare the parameters and computations of SMA-Net with existing networks. Results: The dice similarity coefficient of SMA-Net network for skeletal muscle segmentation was 89.16%, the sensitivity SEN was 88.21%, the positive predictive value PPV was 90.13% and the 95% Hausdorff distance was 5.30mm. Manual segmentation is basically close to the predicted SMA. Our proposed network for cervical cancer patients predicted sarcopenia with 87.5% accuracy, 92.31% precision, 80% recall, 85.72% F1-Score, and 0.871 AUC. The calculation amount of SMA-UNet network is 1.50 GFLOPS, and the parameter amount is 1.24 M. The radiologist’s scores show that minor and no revision accounted for 93.75% on manual revision of skeletal muscle. Conclusion: The lightweight SMA-Net proposed in this study can accurately segment L3 skeletal muscle and quickly calculate its area, which basically meets the clinical application and is convenient for clinical deployment. It is helpful for clinicians to quickly diagnose sarcopenia in patients with cervical cancer, save medical resources, reduce the workload of physicians, and improve diagnostic efficiency.

Thallium bromide (TlBr) is a compound semiconductor material, which can be used for X-ray and gamma-ray detectors and can be used at room temperature. It has excellent physical properties, high atomic number and density, wide bandgap (B = 2.68 eV), and low ionization energy. Compared with other X-ray and gamma-ray detection materials, TlBr devices have high detection efficiency and excellent energy resolution performance. So TlBr is suitable for housing in small tubes or shells, and it can be widely used in nuclear material measurement, safeguards verification, national security, space high-energy physics research, and other fields. Based on the fabrication of TlBr prototype detector, this paper focuses on the device fabrication and signal acquisition technology. Gamma-ray spectrum measurements and performance tests are carried out with AM-241 radioactive source. The results show that the special photoelectric peak of 59.5 keV is clearly visible, and the optimal resolution is 4.15 keV (7%).