Harushi Mori

Tomohiro Kikuchi, Shouhei Hanaoka, Takahiro Nakao et al.

The generation of synthetic medical records using Generative Adversarial Networks (GANs) is becoming crucial for addressing privacy concerns and facilitating data sharing in the medical domain. In this paper, we introduce a novel method to create synthetic hybrid medical records that combine both image and non-image data, utilizing an auto-encoding GAN (alphaGAN) and a conditional tabular GAN (CTGAN). Our methodology encompasses three primary steps: I) Dimensional reduction of images in a private dataset (pDS) using the pretrained encoder of the αGAN, followed by integration with the remaining non-image clinical data to form tabular representations; II) Training the CTGAN on the encoded pDS to produce a synthetic dataset (sDS) which amalgamates encoded image features with non-image clinical data; and III) Reconstructing synthetic images from the image features using the alphaGAN's pretrained decoder. We successfully generated synthetic records incorporating both Chest X-Rays (CXRs) and thirteen non-image clinical variables (comprising seven categorical and six numeric variables). To evaluate the efficacy of the sDS, we designed classification and regression tasks and compared the performance of models trained on pDS and sDS against the pDS test set. Remarkably, by leveraging five times the volume of sDS for training, we achieved classification and regression results that were comparable, if slightly inferior, to those obtained using the native pDS. Our method holds promise for publicly releasing synthetic datasets without undermining the potential for secondary data usage.

Daisuke Matsumoto, Tomohiro Kikuchi, Yusuke Takagi et al.

Rationale and Objectives: Computer-aided detection systems for chest radiographs are widely used, and concurrent reader displays, such as bounding-box (BB) highlights, may influence the reading process. This pilot study used eye tracking to conduct a preliminary experiment to quantify which aspects of visual search were affected. Materials and Methods: We sampled 180 chest radiographs from the VinDR-CXR dataset: 120 with solitary pulmonary nodules or masses and 60 without. The BBs were configured to yield an overall display sensitivity and specificity of 80%. Three radiologists (with 11, 5, and 1 years of experience, respectively) interpreted each case twice - once with BBs visible and once without - after a washout of >= 2 weeks. Eye movements were recorded using an EyeTech VT3 Mini. Metrics included interpretation time, time to first fixation on the lesion, lesion dwell time, total gaze-path length, and lung-field coverage ratio. Outcomes were modeled using a linear mixed model, with reading condition as a fixed effect and case and reader as random intercepts. The primary analysis was restricted to true positives (n=96). Results: Concurrent BB display prolonged interpretation time by 4.9 s (p<0.001) and increased lesion dwell time by 1.3 s (p<0.001). Total gaze-path length increased by 2,076 pixels (p<0.001), and lung-field coverage ratio increased by 10.5% (p<0.001). Time to first fixation on the lesion was reduced by 1.3 s (p<0.001). Conclusion: Eye tracking captured measurable alterations in search behavior associated with concurrent BB displays during chest radiograph interpretation. These findings support the feasibility of this approach and highlight the need for larger studies to confirm effects and explore implications across modalities and clinical contexts.