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Article Open Access

Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence

  • Authors:
    • Maiko Tabuchi
    • Yasuhiko Nakao
    • Hitomi Minami
    • Hiroko Inomata
    • Junya Shiota
    • Taro Akashi
    • Keiichi Hashiguchi
    • Moto Kitayama
    • Kayoko Matsushima
    • Naoyuki Yamaguchi
    • Yuko Akazawa
    • Hisamitsu Miyaaki
  • View Affiliations / Copyright

    Affiliations: Department of Histology and Cell Biology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Nagasaki 852‑8523, Japan, Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Nagasaki 852‑8501, Japan
    Copyright: © Tabuchi et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 76
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    Published online on: January 21, 2026
       https://doi.org/10.3892/etm.2026.13071
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Abstract

Esophageal high‑resolution manometry (HRM) is an important tool for diagnosing and assessing esophageal achalasia. Artificial intelligence (AI)‑assisted HRM image processing has the potential to aid in the diagnosis of esophageal achalasia. However, addressing the challenges associated with the ‘black‑box’ problem is important. In the present study, an automated system that utilizes AI with class‑activation maps to highlight diagnostic areas in HRM images was developed. A total of 211 HRM images, which led to the diagnosis of controls and patients with achalasia, were used to train the system using Resnet34, a convolutional neural network model. The diagnoses included normal, type I achalasia, type II achalasia, type III achalasia and hypercontractile esophagus based on the Chicago classification v3.0 for esophageal motility disorders. A gradient class activation map (Grad‑CAM) technique was used. The discrimination model for the control and achalasia groups yielded a 100% correct response rate for evaluating the validation images (n=30). Grad‑CAM analysis revealed that the model focused on the area around the lower esophageal sphincter pressure in type 1 achalasia for differentiation, closely aligning with expert perspectives. An AI‑based HRM imaging assistance system may not only support physicians in distinguishing esophageal motility disorders with improved diagnostic accuracy but also serve as a novel tool that provides deeper clinical insights and highlights key interpretative features in HRM evaluations. Further large‑scale validation is required to confirm its clinical utility.

View Figures

Figure 1

Learning process involving ResNet-34.
Standard high-resolution manometry images and images from patients
with achalasia were provided as inputs and labeled as ‘Control’ and
‘Achalasia’, respectively. The output represents the probability of
the validation image being classified as either ‘Control’ or
‘Achalasia’. Conv, convolutional layer; fc, fully connected
layer.

Figure 2

Training a ResNet-34 model to
distinguish achalasia from normal esophagus. The left panel shows
the learning process, which was repeated four times using
ResNet-34. The right panel shows validation data with 100% accuracy
across the validation dataset. ‘Train loss’ and ‘valid loss’
indicate the loss values computed for the training and validation
datasets, respectively. The time indicates the duration required
for each epoch (mm:ss).

Figure 3

PCA. Images from PCA using the
TensorBoard software. Black panels represent achalasia cases,
whereas blue panels represent control cases. The spatial separation
between the two groups reflects differences in the learned feature
representations. The spatial separation between the two groups
reflects the difference in the learned feature representations.
PCA, principal component analysis.

Figure 4

Analysis with 3D-tSNE. The figure
shows a combination of principal component analysis and 3D-tSNE
analysis. Black panels represent achalasia cases, whereas blue
panels represent control cases. Notably, an additional subdivision
within the achalasia category was observed, suggesting a more
detailed characterization of the data. 3D-tSNE, three-dimensional
t-distributed stochastic neighbor embedding.

Figure 5

Training of a ResNet-34 model for
classification of achalasia subtypes and hypercontractile
esophagus. The resulting outputs comprised the probabilities of
achalasia types and hypercontractile esophageal classifications
based on validation images. Conv, convolutional layer; fc, fully
connected layer.

Figure 6

Training a ResNet-34 model to
distinguish achalasia typing. The left panel shows the learning
process repeated four times. The right panel shows the validation
data, with an accuracy of 90.9% observed across the validation
dataset for the classification of achalasia type and
hypercontractile esophagus. ‘Train loss’ and ‘valid loss’ indicate
the loss values computed for the training and validation datasets,
respectively. The time indicates the duration required for each
epoch (mm:ss).

Figure 7

Grad-CAM visualization highlighting
regions contributing to AI-based classification. Grad-CAM
visualizations show hotspots in the right panels, representing
regions that the AI focused on to distinguish achalasia from
control images. These hotspots, highlighted by the AI, signify
areas where the disease images differ from the control images and
the percentage indicates the predicted probability for each class
obtained from the softmax output. Grad-CAM, gradient class
activation map program; AI, artificial intelligence.
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Copy and paste a formatted citation
Spandidos Publications style
Tabuchi M, Nakao Y, Minami H, Inomata H, Shiota J, Akashi T, Hashiguchi K, Kitayama M, Matsushima K, Yamaguchi N, Yamaguchi N, et al: <p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>. Exp Ther Med 31: 76, 2026.
APA
Tabuchi, M., Nakao, Y., Minami, H., Inomata, H., Shiota, J., Akashi, T. ... Miyaaki, H. (2026). <p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>. Experimental and Therapeutic Medicine, 31, 76. https://doi.org/10.3892/etm.2026.13071
MLA
Tabuchi, M., Nakao, Y., Minami, H., Inomata, H., Shiota, J., Akashi, T., Hashiguchi, K., Kitayama, M., Matsushima, K., Yamaguchi, N., Akazawa, Y., Miyaaki, H."<p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>". Experimental and Therapeutic Medicine 31.3 (2026): 76.
Chicago
Tabuchi, M., Nakao, Y., Minami, H., Inomata, H., Shiota, J., Akashi, T., Hashiguchi, K., Kitayama, M., Matsushima, K., Yamaguchi, N., Akazawa, Y., Miyaaki, H."<p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>". Experimental and Therapeutic Medicine 31, no. 3 (2026): 76. https://doi.org/10.3892/etm.2026.13071
Copy and paste a formatted citation
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Spandidos Publications style
Tabuchi M, Nakao Y, Minami H, Inomata H, Shiota J, Akashi T, Hashiguchi K, Kitayama M, Matsushima K, Yamaguchi N, Yamaguchi N, et al: <p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>. Exp Ther Med 31: 76, 2026.
APA
Tabuchi, M., Nakao, Y., Minami, H., Inomata, H., Shiota, J., Akashi, T. ... Miyaaki, H. (2026). <p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>. Experimental and Therapeutic Medicine, 31, 76. https://doi.org/10.3892/etm.2026.13071
MLA
Tabuchi, M., Nakao, Y., Minami, H., Inomata, H., Shiota, J., Akashi, T., Hashiguchi, K., Kitayama, M., Matsushima, K., Yamaguchi, N., Akazawa, Y., Miyaaki, H."<p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>". Experimental and Therapeutic Medicine 31.3 (2026): 76.
Chicago
Tabuchi, M., Nakao, Y., Minami, H., Inomata, H., Shiota, J., Akashi, T., Hashiguchi, K., Kitayama, M., Matsushima, K., Yamaguchi, N., Akazawa, Y., Miyaaki, H."<p>Development of a diagnostic model for esophageal achalasia assessed by esophageal high‑resolution manometry using artificial intelligence</p>". Experimental and Therapeutic Medicine 31, no. 3 (2026): 76. https://doi.org/10.3892/etm.2026.13071
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