Next, the team implemented two AI models based on different architectures—UNet and MobileNetV2. They modified the base design of these architectures to enable them to perform both classification (“Is there an unusual object in the CT image?”) and localization (“Where is the unusual object?”). Then, they trained and tested the models using images from the dataset.

Through statistical analyses, the research team evaluated various performance metrics to verify that the model observers could accurately emulate how a human would assess the CT images of the phantom. “Our results were very promising, as both trained models performed remarkably well and achieved an absolute percentage error of less than 5 percent. This indicated that the models could identify the object inserted in the phantom with similar accuracy and confidence as a human professional, for almost all reconstruction configurations and abnormalities sizes and contrasts,” remarked Doria, while discussing their findings.


Doria and her team believe that with additional efforts, their model could become a viable strategy to automatically assess CT image quality. She further adds, “Our CNN-based model observers could greatly simplify the process of optimizing the radiation dose used in CT protocols, thereby minimizing health risks to the patient, and help avoid the time-consuming limitations of medical evaluations.”

Doria expressed confidence that the team will succeed in applying their AI model observers on a larger scale, making CT evaluations faster and safer than ever before.

Read the Open Access article by Valeri et al., “UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images,” J. Med. Imaging 10(S1), S11904 (2023), doi 10.1117/1.JMI.10.S1.S11904.

The article is part of the JMI Special Issue on Medical Image Perception and Observer Performance (currently in progress), guest edited by Elizabeth A. Krupinski, Asli Kumcu, and Karla Evans.

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