循環器ジャーナル71巻3号

2023年07月発行

特集 心臓リハビリテーションのエビデンスを極める Ⅱ.新しい分野の心臓リハビリテーションを知る

AIを駆使した心臓リハビリテーション

著者： 貝原俊樹¹

所属機関： ¹川崎市立多摩病院循環器内科

ページ範囲：P.381 - P.384

POINT
・心リハ領域では，生体データ解析と患者フィードバックに対してAI利活用が期待される．
・AIを心リハへと活用する際，透明性や説明可能性などAI特有の解決すべき課題がある．
・引き続き，心リハ臨床現場におけるAIの実行可能性調査を重ねることが重要である．

参考文献

1）Krittanawong C, Zhang HJ, Wang Z, et al. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol 2017 ; 69 : 2657-64.

2）Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 2019 ; 25 : 65-9.

3）Khurshid S, Friedman S, Reeder C, et al. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation 2022 ; 145 : 122-33.

4）Emakhu J, Monplaisir L, Aguwa C, et al. Acute coronary syndrome prediction in emergency care : A machine learning approach. Comput Methods Programs Biomed 2022 ; 225 : 107080.

5）Kaihara T, Scherrenberg M, Falter M, et al. Cardiac Telerehabilitation-A Solution for Cardiovascular Care in Japan. Circ Rep 2021 ; 3 : 733-6.

6）Shameer K, Johnson WK, Glicksberg SB, et al. Machine learning in cardiovascular medicine : are we there yet? Heart 2018 ; 104 : 1156-64.

7）Scherrenberg M, Bonneux C, Mahmood DY, et al. A Mobile Application to Perform the Six-Minute Walk Test（6MWT）at Home : A Random Walk in the Park Is as Accurate as a Standardized 6MWT. Sensors 2022 ; 22 : 4277.

8）De Cannière H, Corradi F, Smeets CJP, et al. Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation. Sensors（Switzerland）2020 ; 20 : 1-15.

9）Kaihara T, Falter M, Scherrenberg M, et al. The impact of dietary education and counselling with a smartphone application on secondary prevention of coronary artery disease : A randomised controlled study（The TeleDiet Study）. Digit Health 2023 ; 9 : 20552076231164101.

10）Lowres N, Duckworth A, Redfern J, et al. Use of a machine learning program to correctly triage incoming text messaging replies from a cardiovascular text-based secondary prevention program : Feasibility study. JMIR Mhealth Uhealth 2020 ; 8 : e19200.

11）Young L, Zhang Q, Lian E, et al. Factors predicting the utilization of center-based cardiac rehabilitation program. Geriatrics（Basel）2020 ; 5 : 66.

12）Kumar Y, Koul A, Singla R, et al. Artificial intelligence in disease diagnosis : a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput 2022 ; 1-28. doi : 10.1007/s12652-021-03612-z（Online ahead of print）.

13）Stein N, Brooks K. A fully automated conversational artificial intelligence for weight loss : Longitudinal observational study among overweight and obese adults. JMIR Diabetes 2017 ; 2 : e28.

14）Maher CA, Davis CR, Curtis RG, et al. A physical activity and diet program delivered by artificially intelligent virtual health coach : Proof-of-concept study. JMIR Mhealth Uhealth 2020 ; 8 : e17558.

15）Perski O, Crane D, Beard E, et al. Does the addition of a supportive chatbot promote user engagement with a smoking cessation app? An experimental study. Digit Health 2019 ; 5 : 2055207619880676.

16）Olano-Espinosa E, Avila-Tomas JF, Minue-Lorenzo C, et al. Effectiveness of a Conversational Chatbot (Dejal@bot) for the Adult Population to Quit Smoking : Pragmatic, Multicenter, Controlled, Randomized Clinical Trial in Primary Care. JMIR Mhealth Uhealth 2022 ; 10 : e34273.

17）Reddy S. Explainability and artificial intelligence in medicine. Lancet Digit Health 2022 ; 4 : e214-5.

18）Asan O, Bayrak AE, Choudhury A. Artificial Intelligence and Human Trust in Healthcare : Focus on Clinicians. J Med Internet Res 2020 ; 22 : e15154.

19）Char SD, Abràmoff DM, Feudtner C. Identifying Ethical Considerations for Machine Learning Healthcare Applications. Am J Bioeth 2020 ; 20 : 7-17.

20）Tran VT, Riveros C, Ravaud P. Patients' views of wearable devices and AI in healthcare : findings from the ComPaRe e-cohort. NPJ Digit Med 2019 ; 2 : 53.