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<p>This study explored the feasibility of developing a model that can diagnose positive and negative bone metastasis from bone scan images using Teachable Machine by Google, a no-code AI platform that does not require programming skills or a GPU environment. A fourth-year medical student trained deep learning models using a Teachable Machine on a dataset of 4626 bone scan images from patients with cancer (mean age 65.1 ± 11.3&#xa0;years; 50.5% female). Because of severe class imbalance (bone metastasis positive:negative = 400:4226), we compared the diagnostic performance of two strategies (original set and augmented dataset with tenfold data augmentation applied to positive images). We investigated the diagnostic performance using various hyperparameters (epochs 50–1000, batch sizes 16–32) with a learning rate of 0.001. The final model generated by Teachable Machine was compared with a conventional deep learning model based on ResNet50. The combination of epoch = 150 and batch size = 16 showed the optimal diagnostic performance. The overall sensitivity, specificity, and positive and negative predictive values were 57.1%, 93.9%, 90.4%, and 68.7%, respectively. Both Teachable Machine and ResNet50 showed good diagnostic performance (area under the curve = 0.812 and 0.869, respectively), although the diagnostic performance of Teachable Machine was inferior to that of the conventional ResNet50 model (<i>p</i> &lt; 0.001). Given its convenience, Teachable Machine represents a valuable and accessible tool for medical education and preliminary model development. It allows researchers without programming skills or GPU resources to construct feasibility models for medical image classification.</p>

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Feasibility of No-Code Deep Learning for Diagnosing Bone Metastasis in Bone Scans: A Comparative Study of Teachable Machine and ResNet

  • 期刊论文

  • 发表日期：

    2026年5月1日

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  • Sehyun Pak,
  • Ji Young Woo,
  • Ik Yang,
  • Hye Joo Son,
  • Soo-Jong Kim,
  • Suk Hyun Lee

摘要

This study explored the feasibility of developing a model that can diagnose positive and negative bone metastasis from bone scan images using Teachable Machine by Google, a no-code AI platform that does not require programming skills or a GPU environment. A fourth-year medical student trained deep learning models using a Teachable Machine on a dataset of 4626 bone scan images from patients with cancer (mean age 65.1 ± 11.3 years; 50.5% female). Because of severe class imbalance (bone metastasis positive:negative = 400:4226), we compared the diagnostic performance of two strategies (original set and augmented dataset with tenfold data augmentation applied to positive images). We investigated the diagnostic performance using various hyperparameters (epochs 50–1000, batch sizes 16–32) with a learning rate of 0.001. The final model generated by Teachable Machine was compared with a conventional deep learning model based on ResNet50. The combination of epoch = 150 and batch size = 16 showed the optimal diagnostic performance. The overall sensitivity, specificity, and positive and negative predictive values were 57.1%, 93.9%, 90.4%, and 68.7%, respectively. Both Teachable Machine and ResNet50 showed good diagnostic performance (area under the curve = 0.812 and 0.869, respectively), although the diagnostic performance of Teachable Machine was inferior to that of the conventional ResNet50 model (p < 0.001). Given its convenience, Teachable Machine represents a valuable and accessible tool for medical education and preliminary model development. It allows researchers without programming skills or GPU resources to construct feasibility models for medical image classification.