高齢女性の下腿のデジタル画像による低骨格筋肉量判定の可能性 Convolutional Neural Network とエッジ検出を用いた分類による予備的研究

【目的】下腿のデジタル画像とエッジ検出により低Skeletal Muscle Index（以下，SMI）である高齢女性を判定できるか予備的に検証すること。【方法】入院中の高齢女性を対象とした。デジタルカメラで撮影した対象者の下腿のデジタル画像を用いて，キャニー法でエッジ検出を行った。低SMI の基準はアジア作業グループが提唱する基準値5.7 kg/m2 を用いて群分けを行い，下腿のデジタル画像とエッジ検出した画像のそれぞれで，Convolutional Neural Network による解析を実施した。【結果】対象者は32 名であった。下腿のデジタル画像およびエッジ検出した画像における低SM...

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Published in理学療法学 Vol. 48; no. 3; pp. 279 - 286
Main Authors 中口, 拓真, 近藤, 義剛, 桑田, 一記, 田津原, 佑介, 福本, 祐真, 石本, 泰星
Format Journal Article
LanguageJapanese
Published 日本理学療法士学会 2021
日本理学療法士協会
Subjects
機械学習
畳み込みニューラルネットワーク
骨格筋量
高齢者
Online AccessGet full text
ISSN0289-3770
2189-602X
DOI10.15063/rigaku.11970

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Abstract 【目的】下腿のデジタル画像とエッジ検出により低Skeletal Muscle Index（以下，SMI）である高齢女性を判定できるか予備的に検証すること。【方法】入院中の高齢女性を対象とした。デジタルカメラで撮影した対象者の下腿のデジタル画像を用いて，キャニー法でエッジ検出を行った。低SMI の基準はアジア作業グループが提唱する基準値5.7 kg/m2 を用いて群分けを行い，下腿のデジタル画像とエッジ検出した画像のそれぞれで，Convolutional Neural Network による解析を実施した。【結果】対象者は32 名であった。下腿のデジタル画像およびエッジ検出した画像における低SMI を判定するC 統計量はそれぞれ0.83（95%CI：0.83–1.00）と0.92（95%CI：0.92–1.00）であった。【結論】下腿のデジタル画像を用いることで低SMI 者を判定できる可能性がある。
AbstractList 【目的】下腿のデジタル画像とエッジ検出により低Skeletal Muscle Index（以下，SMI）である高齢女性を判定できるか予備的に検証すること。【方法】入院中の高齢女性を対象とした。デジタルカメラで撮影した対象者の下腿のデジタル画像を用いて，キャニー法でエッジ検出を行った。低SMI の基準はアジア作業グループが提唱する基準値5.7 kg/m2 を用いて群分けを行い，下腿のデジタル画像とエッジ検出した画像のそれぞれで，Convolutional Neural Network による解析を実施した。【結果】対象者は32 名であった。下腿のデジタル画像およびエッジ検出した画像における低SMI を判定するC 統計量はそれぞれ0.83（95%CI：0.83–1.00）と0.92（95%CI：0.92–1.00）であった。【結論】下腿のデジタル画像を用いることで低SMI 者を判定できる可能性がある。
「要旨」【目的】下腿のデジタル画像とエッジ検出により低Skeletal Muscle Index (以下, SMI) である高齢女性を判定できるか予備的に検証すること. 【方法】入院中の高齢女性を対象とした. デジタルカメラで撮影した対象者の下腿のデジタル画像を用いて, キャニー法でエッジ検出を行った. 低SMIの基準はアジア作業グループが提唱する基準値5.7kg/m2 を用いて群分けを行い, 下腿のデジタル画像とエッジ検出した画像のそれぞれで, Convolutional Neural Networkによる解析を実施した. 【結果】対象者は32名であった. 下腿のデジタル画像およびエッジ検出した画像における低SMIを判定するC統計量はそれぞれ0.83 (95%CI : 0.83-1.00) と0.92 (95%CI : 0.92-1.00) であった. 【結論】下腿のデジタル画像を用いることで低SMI者を判定できる可能性がある.
Author 石本, 泰星
桑田, 一記
田津原, 佑介
福本, 祐真
近藤, 義剛
中口, 拓真
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References 2) Bischoff-Ferrari HA, Orav JE, et al.: Comparative performance of current definitions of sarcopenia against the prospective incidence of falls among community-dwelling seniors age 65 and older. Osteoporos Int. 2015; 26: 2793–2802.
6) Malmstrom TK, Miller DK, et al.: SARC-F: a symptom score to predict persons with sarcopenia at risk for poor functional outcomes. J Cachexia Sarcopenia Muscle. 2016; 7: 28–36.
9) Riccardo A, Barbara Le: When Reporting on Older Patients with Cancer, Frailty Information Is Needed. Ann Surg Oncol. 2011; 18: 4–5.
14) Giuseppe S, Marina D, et al.: Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res. 2017; 29: 591–597.
23) 伊藤　忠，酒井義人，他：入院高齢患者における下腿最大周径による四肢筋量の簡易推定式．理学療法科学．2016; 31(4): 511–515
20) Malmstrom K, Morley J: SARC-F: a simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc. 2013; 14(8): 531–532.
22) Tanaka T, Takahashi K, et al.: "Yubi-wakka" (finger-ring) test: A practical self-screening method for sarcopenia, and a predictor of disability and mortality among Japanese community-dwelling older adults. Geriatr Gerontol Int. 2018; 18: 224–232.
16) Yoshimura Y, Wakabayashi H, et al.: Interventions for Treating Sarcopenia: A Systematic Review and Meta-Analysis of Randomized Controlled Studies. J Am Med Dir Assoc. 2017; 18(6): 553.e1–553.e16.
32) Lecun Y, Bengio Y, et al.: Deep learning. Nature. 2015; 521: 436–444.
8) Stefanie L, Mirko P, et al.: Validation of the FNIH sarcopenia criteria and SOF frailty index as predictors of long-term mortality in ambulatory older men. Age Ageing. 2016; 45: 602–608.
31) International Conference on Learning Representations homepage: On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. https://openreview.net/forum?id=H1oyRlYgg.（2020 年10 月17 日引用）
11) Goodpaster BH, Kelley DE, et al.: Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol. 2000; 1: 104–110.
3) Schaap LA, Schoor MN, et al.: Associations of Sarcopenia Definitions, and Their Components, With the Incidence of Recurrent Falling and Fractures: The Longitudinal Aging Study Amsterdam. J Gerontol A Biol Sci Med Sci. 2018; 10: 1199–1204.
15) Yuguchi S, Asahi R, et al.: Gastrocnemius Thickness by Ultrasonography Indicates the Low Skeletal Muscle Mass in Japanese Elderly People. Arch Gerontol Geriatr. 2020; 20: 104093.
29) Parthasarathy G, Ramanathan L, et al.: Predicting Source and Age of Brain Tumor Using Canny Edge Detection Algorithm and Threshold Technique. Asian Pac J Cancer Prev. 2019; 25: 1409–1414.
4) Bahat G, IIhan B, et al.: Sarcopenia and the cardiometabolic syndrome: A narrative review. Eur Geriatr Med. 2016; 7: 220–223.
13) Mitsiopoulos N, Baumgartner RN, et al.: Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol. 1998; 85: 115–122.
17) Hanach N, McCullough F, et al.: The Impact of Dairy Protein Intake on Muscle Mass, Muscle Strength, and Physical Performance in Middle-Aged to Older Adults with or without Existing Sarcopenia: A Systematic Review and Meta-Analysis. Adv Nutr. 2019; 10(1): 59–69.
19) Yang M, Xiaoyi H, et al.: SARC-F for Sarcopenia Screening in Community-Dwelling Older Adults: Are 3 Items Enough? Medicine. 2018; 9730: 11726.
26) Gulshan V, Peng L, et al.: Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Jama. 2016; 316: 2402–2410.
10) Vicente V, Neda A, et al.: Sarcopenia Adversely Impacts Postoperative Complications Following Resection or Transplantation in Patients with Primary Liver Tumors. J Gastrointest Surg. 2015; 19: 272–281.
28) Syed Muhammad A, Muhammad M, et al.: Medical Image Analysis Using Convolutional Neural Networks: A Review. J Med Syst. 2018; 8: 226.
27) Ehteshami B, Veta M, et al.: Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. Jama. 2017; 338; 2199–2210.
5) Bone AE, Hepgul N, et al.: Sarcopenia and frailty in chronic respiratory disease. Chron Respir Dis. 2017; 14: 85–99.
25) Ribli D, Horvath A, et al.: Detecting and classifying lesions in mammograms with Deep Learning. Sci Rep. 2018; 8: 4165.
18) Gielem E, Beckwee D, et al.: Nutritional interventions to improve muscle mass, muscle strength, and physical performance in older people: an umbrella review of systematic reviews and meta-analyses. 2020; nuaa011.
1) Chen LK, Woo J, et al.: Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020; 21: 300–307.
21) Yang M, Xiaoyi H, et al.: Screening Sarcopenia in Community-Dwelling Older Adults: SARC-F vs SARC-F Combined With Calf Circumference (SARC-CalF). J Am Med Dir Assoc. 2018; 19: 277.e1–277.e8.
30) Honda H, Qureshi A, et al.: Obese sarcopenia in patients with end-stage renal disease is associated with inflammation and increased mortality. Am J Clin Nutr. 2007; 86: 633–638.
24) Kawakami R, Murakami H, et al.: Calf circumference as a surrogate marker of muscle mass for diagnosing sarcopenia in Japanese men and women. Geriatr Gerontol Int. 2015; 15(8): 969–976.
35) Eugene B, Anastasia G, et al.: PhotoAgeClock: Deep Learning Algorithms for Development of Non-Invasive Visual Biomarkers of Aging. Aging (Albany NY). 2018: 9: 3249–3259.
33) Villani M, Maria C, et al.: Appendicular Skeletal Muscle in Hospitalised Hip-Fracture Patients: Development and Cross-Validation of Anthropometric Prediction Equations Against Dual-Energy X-ray Absorptiometry. Age Ageing. 2014; 43: 857–862.
34) Ida S, Murata K, et al.: Development of a Japanese Version of the SARC-F for Diabetic Patients: An Examination of Reliability and Validity. Aging Clin Exp Res. 2017; 29: 935–939.
12) Schweitzer L, Geisler C, et al.: What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults? Am J Clin Nutr. 2015; 102: 58–65.
7) Beaudart C, Emmanuel B, et al.: Validation of the SarQoL, a specific health-related quality of life questionnaire for Sarcopenia. J Cachexia Sarcopenia Muscle. 2017; 8: 238–244.
36) Mijnarends DM, Meijers M, et al.: Validity and reliability of tools to measure muscle mass, strength, and physical performance in community-dwelling older people: a systematic review. J Am Med Dir Assoc. 2013; 14(3): 170–178.
References_xml – reference: 12) Schweitzer L, Geisler C, et al.: What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults? Am J Clin Nutr. 2015; 102: 58–65.
– reference: 21) Yang M, Xiaoyi H, et al.: Screening Sarcopenia in Community-Dwelling Older Adults: SARC-F vs SARC-F Combined With Calf Circumference (SARC-CalF). J Am Med Dir Assoc. 2018; 19: 277.e1–277.e8.
– reference: 2) Bischoff-Ferrari HA, Orav JE, et al.: Comparative performance of current definitions of sarcopenia against the prospective incidence of falls among community-dwelling seniors age 65 and older. Osteoporos Int. 2015; 26: 2793–2802.
– reference: 32) Lecun Y, Bengio Y, et al.: Deep learning. Nature. 2015; 521: 436–444.
– reference: 4) Bahat G, IIhan B, et al.: Sarcopenia and the cardiometabolic syndrome: A narrative review. Eur Geriatr Med. 2016; 7: 220–223.
– reference: 24) Kawakami R, Murakami H, et al.: Calf circumference as a surrogate marker of muscle mass for diagnosing sarcopenia in Japanese men and women. Geriatr Gerontol Int. 2015; 15(8): 969–976.
– reference: 33) Villani M, Maria C, et al.: Appendicular Skeletal Muscle in Hospitalised Hip-Fracture Patients: Development and Cross-Validation of Anthropometric Prediction Equations Against Dual-Energy X-ray Absorptiometry. Age Ageing. 2014; 43: 857–862.
– reference: 18) Gielem E, Beckwee D, et al.: Nutritional interventions to improve muscle mass, muscle strength, and physical performance in older people: an umbrella review of systematic reviews and meta-analyses. 2020; nuaa011.
– reference: 26) Gulshan V, Peng L, et al.: Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Jama. 2016; 316: 2402–2410.
– reference: 28) Syed Muhammad A, Muhammad M, et al.: Medical Image Analysis Using Convolutional Neural Networks: A Review. J Med Syst. 2018; 8: 226.
– reference: 29) Parthasarathy G, Ramanathan L, et al.: Predicting Source and Age of Brain Tumor Using Canny Edge Detection Algorithm and Threshold Technique. Asian Pac J Cancer Prev. 2019; 25: 1409–1414.
– reference: 8) Stefanie L, Mirko P, et al.: Validation of the FNIH sarcopenia criteria and SOF frailty index as predictors of long-term mortality in ambulatory older men. Age Ageing. 2016; 45: 602–608.
– reference: 10) Vicente V, Neda A, et al.: Sarcopenia Adversely Impacts Postoperative Complications Following Resection or Transplantation in Patients with Primary Liver Tumors. J Gastrointest Surg. 2015; 19: 272–281.
– reference: 16) Yoshimura Y, Wakabayashi H, et al.: Interventions for Treating Sarcopenia: A Systematic Review and Meta-Analysis of Randomized Controlled Studies. J Am Med Dir Assoc. 2017; 18(6): 553.e1–553.e16.
– reference: 13) Mitsiopoulos N, Baumgartner RN, et al.: Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol. 1998; 85: 115–122.
– reference: 17) Hanach N, McCullough F, et al.: The Impact of Dairy Protein Intake on Muscle Mass, Muscle Strength, and Physical Performance in Middle-Aged to Older Adults with or without Existing Sarcopenia: A Systematic Review and Meta-Analysis. Adv Nutr. 2019; 10(1): 59–69.
– reference: 22) Tanaka T, Takahashi K, et al.: "Yubi-wakka" (finger-ring) test: A practical self-screening method for sarcopenia, and a predictor of disability and mortality among Japanese community-dwelling older adults. Geriatr Gerontol Int. 2018; 18: 224–232.
– reference: 7) Beaudart C, Emmanuel B, et al.: Validation of the SarQoL, a specific health-related quality of life questionnaire for Sarcopenia. J Cachexia Sarcopenia Muscle. 2017; 8: 238–244.
– reference: 1) Chen LK, Woo J, et al.: Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020; 21: 300–307.
– reference: 5) Bone AE, Hepgul N, et al.: Sarcopenia and frailty in chronic respiratory disease. Chron Respir Dis. 2017; 14: 85–99.
– reference: 31) International Conference on Learning Representations homepage: On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. https://openreview.net/forum?id=H1oyRlYgg.（2020 年10 月17 日引用）
– reference: 14) Giuseppe S, Marina D, et al.: Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res. 2017; 29: 591–597.
– reference: 25) Ribli D, Horvath A, et al.: Detecting and classifying lesions in mammograms with Deep Learning. Sci Rep. 2018; 8: 4165.
– reference: 3) Schaap LA, Schoor MN, et al.: Associations of Sarcopenia Definitions, and Their Components, With the Incidence of Recurrent Falling and Fractures: The Longitudinal Aging Study Amsterdam. J Gerontol A Biol Sci Med Sci. 2018; 10: 1199–1204.
– reference: 6) Malmstrom TK, Miller DK, et al.: SARC-F: a symptom score to predict persons with sarcopenia at risk for poor functional outcomes. J Cachexia Sarcopenia Muscle. 2016; 7: 28–36.
– reference: 23) 伊藤　忠，酒井義人，他：入院高齢患者における下腿最大周径による四肢筋量の簡易推定式．理学療法科学．2016; 31(4): 511–515．
– reference: 11) Goodpaster BH, Kelley DE, et al.: Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol. 2000; 1: 104–110.
– reference: 9) Riccardo A, Barbara Le: When Reporting on Older Patients with Cancer, Frailty Information Is Needed. Ann Surg Oncol. 2011; 18: 4–5.
– reference: 15) Yuguchi S, Asahi R, et al.: Gastrocnemius Thickness by Ultrasonography Indicates the Low Skeletal Muscle Mass in Japanese Elderly People. Arch Gerontol Geriatr. 2020; 20: 104093.
– reference: 36) Mijnarends DM, Meijers M, et al.: Validity and reliability of tools to measure muscle mass, strength, and physical performance in community-dwelling older people: a systematic review. J Am Med Dir Assoc. 2013; 14(3): 170–178.
– reference: 27) Ehteshami B, Veta M, et al.: Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. Jama. 2017; 338; 2199–2210.
– reference: 20) Malmstrom K, Morley J: SARC-F: a simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc. 2013; 14(8): 531–532.
– reference: 30) Honda H, Qureshi A, et al.: Obese sarcopenia in patients with end-stage renal disease is associated with inflammation and increased mortality. Am J Clin Nutr. 2007; 86: 633–638.
– reference: 19) Yang M, Xiaoyi H, et al.: SARC-F for Sarcopenia Screening in Community-Dwelling Older Adults: Are 3 Items Enough? Medicine. 2018; 9730: 11726.
– reference: 34) Ida S, Murata K, et al.: Development of a Japanese Version of the SARC-F for Diabetic Patients: An Examination of Reliability and Validity. Aging Clin Exp Res. 2017; 29: 935–939.
– reference: 35) Eugene B, Anastasia G, et al.: PhotoAgeClock: Deep Learning Algorithms for Development of Non-Invasive Visual Biomarkers of Aging. Aging (Albany NY). 2018: 9: 3249–3259.
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Snippet 【目的】下腿のデジタル画像とエッジ検出により低Skeletal Muscle Index（以下，SMI）である高齢女性を判定できるか予備的に検証すること。【方法】入院中の高齢女性を対象とした。デジタルカメラで撮影した対象者の下腿のデジタル画像を用いて，キャニー法でエッジ検出を行った。低SMI...
「要旨」【目的】下腿のデジタル画像とエッジ検出により低Skeletal Muscle Index (以下, SMI) である高齢女性を判定できるか予備的に検証すること. 【方法】入院中の高齢女性を対象とした. デジタルカメラで撮影した対象者の下腿のデジタル画像を用いて, キャニー法でエッジ検出を行った....
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jstage
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SubjectTerms 機械学習
畳み込みニューラルネットワーク
骨格筋量
高齢者
Subtitle Convolutional Neural Network とエッジ検出を用いた分類による予備的研究
Title 高齢女性の下腿のデジタル画像による低骨格筋肉量判定の可能性
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ispartofPNX 理学療法学, 2021, Vol.48(3), pp.279-286
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