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Objectives: Developing effective Convolutional Neural Networks (CNN) for soft tissue sarcoma detection often requires numerous iterations and adjustments, demanding specialized IT (Information Technology) skills. This study aims to use ChatGPT 4 to simplify CNN adaptation, reducing the need for specialized IT skills while enabling efficient exploration of training configurations to enhance diagnostic accuracy. Methods: This study leveraged a preexisting Artificial Intelligence (AI) model adapted using a preexisting Convolutional Neural Network (CNN). The study involved 54 participants diagnosed with primary soft tissue sarcomas in the extremities and possessing complete Magnetic Resonance Imaging (MRI) datasets. AI adaptations and programming were conducted using TensorFlow and verified with ChatGPT. Model training involved a dataset split of 70% training, 15% validation and 15% test set on patient level split, processed over eight epochs. Results: The adapted CNN model demonstrated significant improvement across various MRI sequences, achieving high accuracy levels (up to 98.5%) and excellent sensitivity and specificity rates. The model performed robustly in differentiating tumor presence in MR images, with test accuracies as high as 93.9%. The inclusion of a Gradient-weighted Class Activation Mapping (Grad-CAM) heat map and probability scores in the diagnostic outputs further enhanced interpretative capabilities. Conclusion: This study highlights the potential of AI, particularly CNNs, in the early and accurate detection of soft tissue sarcomas, underscoring the technology's adaptability across different imaging modalities. The integration of large language models like ChatGPT into the model adaptation process emphasizes the reduced need for specialized IT skills, making advanced diagnostic tools more accessible and potentially improving diagnostic accuracy and patient outcomes in radiology and oncology.
Voigtländer et al. (Mon,) studied this question.