Exploratory machine learning analysis to characterize angioscopic features associated with atherosclerosis-related aortic dissection: an exploratory single-center angioscopic study - Scorecard - MDSpire

Exploratory machine learning analysis to characterize angioscopic features associated with atherosclerosis-related aortic dissection: an exploratory single-center angioscopic study

  • By

  • Satoru Takahashi

  • Sei Komatsu

  • Chikao Yutani

  • Hiroyuki Nishi

  • Yoshiharu Higuchi

  • Nobuzo Iwa

  • Tomoki Ohara

  • Mitsuhiko Takewa

  • Kazuhisa Kodama

  • May 7, 2026

  • 0 min

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Clinical Scorecard: Investigation of Machine Learning Techniques to Identify Angioscopic Characteristics Linked to Atherosclerosis-Induced Aortic Dissection: A Single-Center Exploratory Study

At a Glance

CategoryDetail
Condition
Key Mechanisms
Target PopulationPatients diagnosed with Aortic Dissection and those with coronary artery disease, including specific inclusion criteria.
Care Setting

Key Highlights

  • fissure_bleeding

Guideline-Based Recommendations

Diagnosis

    Management

      Monitoring & Follow-up

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      Risks

        Patient & Prescribing Data

        Patients with Aortic Dissection and coronary artery disease

        NOGA may improve early detection and management of aortic injuries.

        Clinical Best Practices

        • ethical_considerations

        Related Resources & Content

        Original Source(s)

        Exploratory machine learning analysis to characterize angioscopic features associated with atherosclerosis-related aortic dissection: an exploratory single-center angioscopic study

        • by Satoru Takahashi, Sei Komatsu, Chikao Yutani, Hiroyuki Nishi, Yoshiharu Higuchi, Nobuzo Iwa, Tomoki Ohara, Mitsuhiko Takewa, Kazuhisa Kodama

        • May 7, 2026

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