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Howell RS, Liu HH, Khan AA, Woods JS, Lin LJ, Saxena M, Saxena H, Castellano M, Petrone P, Slone E, Chiu ES, Gillette BM, Gorenstein SA. Development of a Method for Clinical Evaluation of Artificial Intelligence-Based Digital Wound Assessment Tools. JAMA Netw Open 2021; 4:e217234. [PMID: 34009348 PMCID: PMC8134996 DOI: 10.1001/jamanetworkopen.2021.7234] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
IMPORTANCE Accurate assessment of wound area and percentage of granulation tissue (PGT) are important for optimizing wound care and healing outcomes. Artificial intelligence (AI)-based wound assessment tools have the potential to improve the accuracy and consistency of wound area and PGT measurement, while improving efficiency of wound care workflows. OBJECTIVE To develop a quantitative and qualitative method to evaluate AI-based wound assessment tools compared with expert human assessments. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was performed across 2 independent wound centers using deidentified wound photographs collected for routine care (site 1, 110 photographs taken between May 1 and 31, 2018; site 2, 89 photographs taken between January 1 and December 31, 2019). Digital wound photographs of patients were selected chronologically from the electronic medical records from the general population of patients visiting the wound centers. For inclusion in the study, the complete wound edge and a ruler were required to be visible; circumferential ulcers were specifically excluded. Four wound specialists (2 per site) and an AI-based wound assessment service independently traced wound area and granulation tissue. MAIN OUTCOMES AND MEASURES The quantitative performance of AI tracings was evaluated by statistically comparing error measure distributions between test AI traces and reference human traces (AI vs human) with error distributions between independent traces by 2 humans (human vs human). Quantitative outcomes included statistically significant differences in error measures of false-negative area (FNA), false-positive area (FPA), and absolute relative error (ARE) between AI vs human and human vs human comparisons of wound area and granulation tissue tracings. Six masked attending physician reviewers (3 per site) viewed randomized area tracings for AI and human annotators and qualitatively assessed them. Qualitative outcomes included statistically significant difference in the absolute difference between AI-based PGT measurements and mean reviewer visual PGT estimates compared with PGT estimate variability measures (ie, range, standard deviation) across reviewers. RESULTS A total of 199 photographs were selected for the study across both sites; mean (SD) patient age was 64 (18) years (range, 17-95 years) and 127 (63.8%) were women. The comparisons of AI vs human with human vs human for FPA and ARE were not statistically significant. AI vs human FNA was slightly elevated compared with human vs human FNA (median [IQR], 7.7% [2.7%-21.2%] vs 5.7% [1.6%-14.9%]; P < .001), indicating that AI traces tended to slightly underestimate the human reference wound boundaries compared with human test traces. Two of 6 reviewers had a statistically higher frequency in agreement that human tracings met the standard area definition, but overall agreement was moderate (352 yes responses of 583 total responses [60.4%] for AI and 793 yes responses of 1166 total responses [68.0%] for human tracings). AI PGT measurements fell in the typical range of variation in interreviewer visual PGT estimates; however, visual PGT estimates varied considerably (mean range, 34.8%; mean SD, 19.6%). CONCLUSIONS AND RELEVANCE This study provides a framework for evaluating AI-based digital wound assessment tools that can be extended to automated measurements of other wound features or adapted to evaluate other AI-based digital image diagnostic tools. As AI-based wound assessment tools become more common across wound care settings, it will be important to rigorously validate their performance in helping clinicians obtain accurate wound assessments to guide clinical care.
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Affiliation(s)
- Raelina S. Howell
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
| | - Helen H. Liu
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
| | - Aziz A. Khan
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
| | - Jon S. Woods
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
| | - Lawrence J. Lin
- NYU Kimmel Hyperbaric and Advanced Wound Healing Center, New York, New York
| | | | | | - Michael Castellano
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
- Department of Surgery, NYU Long Island School of Medicine, Mineola, New York
| | - Patrizio Petrone
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
- Department of Surgery, NYU Long Island School of Medicine, Mineola, New York
| | - Eric Slone
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
| | - Ernest S. Chiu
- NYU Kimmel Hyperbaric and Advanced Wound Healing Center, New York, New York
| | - Brian M. Gillette
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
- Department of Foundations of Medicine, NYU Long Island School of Medicine, Mineola, New York
| | - Scott A. Gorenstein
- Department of Surgery, NYU Langone Hospital Long Island, Mineola, New York
- Department of Surgery, NYU Long Island School of Medicine, Mineola, New York
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Nagata T, Noyori SS, Noguchi H, Nakagami G, Kitamura A, Sanada H. Skin tear classification using machine learning from digital RGB image. J Tissue Viability 2021; 30:588-593. [PMID: 33902993 DOI: 10.1016/j.jtv.2021.01.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 12/16/2020] [Accepted: 01/08/2021] [Indexed: 11/28/2022]
Abstract
AIM Skin tears are traumatic wounds characterised by separation of the skin layers. Severity evaluation is important in the management of skin tears. To support the assessment and management of skin tears, this study aimed to develop an algorithm to estimate a category of the Skin Tear Audit Research classification system (STAR classification) using digital images via machine learning. This was achieved by introducing shape features representing complicated shape of the skin tears. METHODS A skin tear image was separated into small segments, and features of each segment were estimated. The segments were then classified into different classes by machine learning algorithms, namely support vector machine and random forest. Their performance in classifying wound segments and STAR categories was evaluated with 31 images using the leave-one-out cross validation. RESULTS Support vector machine showed an accuracy of 74% and 69% in classifying wound segments and STAR categories, respectively. The corresponding accuracy using random forest were 71% and 63%. CONCLUSION Machine learning algorithms revealed capable of classifying categories of skin tears. This could offer the potential to aid nurses in their management of skin tears, even if they are not specialised in wound care.
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Affiliation(s)
- Takuro Nagata
- School of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Shuhei S Noyori
- Department of Gerontological Nursing/Wound Care Management, The Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Graduate Program for Social ICT Global Creative Leaders, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan.
| | - Hiroshi Noguchi
- Department of Life Support Technology (Molten), Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Gojiro Nakagami
- Department of Gerontological Nursing/Wound Care Management, The Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Aya Kitamura
- Department of Gerontological Nursing/Wound Care Management, The Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Hiromi Sanada
- Department of Gerontological Nursing/Wound Care Management, The Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
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Terashima T, Yoshimura S. Skin colour assessment of replanted fingers in digital images and its reliability for the incorporation of images in nursing progress notes. J Clin Nurs 2017; 27:e1225-e1232. [PMID: 29266698 DOI: 10.1111/jocn.14225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2017] [Indexed: 11/28/2022]
Abstract
AIMS AND OBJECTIVES To determine whether nurses can accurately assess the skin colour of replanted fingers displayed as digital images on a computer screen. BACKGROUND Colour measurement and clinical diagnostic methods for medical digital images have been studied, but reproducing skin colour on a computer screen remains difficult. DESIGN The inter-rater reliability of skin colour assessment scores was evaluated. In May 2014, 21 nurses who worked on a trauma ward in Japan participated in testing. METHODS Six digital images with different skin colours were used. Colours were scored from both digital images and direct patient's observation. The score from a digital image was defined as the test score, and its difference from the direct assessment score as the difference score. Intraclass correlation coefficients were calculated. Nurses' opinions were classified and summarised. RESULTS The intraclass correlation coefficients for the test scores were fair. Although the intraclass correlation coefficients for the difference scores were poor, they improved to good when three images that might have contributed to poor reliability were excluded. Most nurses stated that it is difficult to assess skin colour in digital images; they did not think it could be a substitute for direct visual assessment. However, most nurses were in favour of including images in nursing progress notes. DISCUSSION Although the inter-rater reliability was fairly high, the reliability of colour reproduction in digital images as indicated by the difference scores was poor. Nevertheless, nurses expect the incorporation of digital images in nursing progress notes to be useful. This gap between the reliability of digital colour reproduction and nurses' expectations towards it must be addressed. CONCLUSIONS High inter-rater reliability for digital images in nursing progress notes was not observed. Assessments of future improvements in colour reproduction technologies are required. RELEVANCE TO CLINICAL PRACTICE Further digitisation and visualisation of nursing records might pose challenges.
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Affiliation(s)
- Taiko Terashima
- Japanese Red Cross Hokkaido College of Nursing, Kitami, Hokkaido, Japan
| | - Sadako Yoshimura
- Department of Nursing, Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, Japan
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Galluccio F, Allanore Y, Czirjak L, Furst DE, Khanna D, Matucci-Cerinic M. Points to consider for skin ulcers in systemic sclerosis. Rheumatology (Oxford) 2017; 56:v67-v71. [PMID: 28992171 DOI: 10.1093/rheumatology/kex200] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Indexed: 12/22/2022] Open
Abstract
This article discusses points to consider when undertaking a clinical trial to test therapy for skin ulcers in SSc. A validated definition of skin ulcers should be used if available. Defining a uniform SSc patient population, including consideration of disease duration, history of digital ulcers and capillaroscopic patterns, is important. Excluding confounding factors such as infection, calcinosis and trauma should be strongly considered, or at least accounted for, in defining patients. Outcome measures such as time to healing, prevention of new ulcers, function, pain and objective measures such as US, laser Doppler and thermography can be considered as outcome measures, although their validation has not yet been achieved and efforts may be needed to validate them before use. Likewise, biomarkers should be considered or consideration should be given to storing serum, plasma or cells for possible future analysis. A pre-planned analysis is important and should include consideration of missing data.
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Affiliation(s)
- Felice Galluccio
- Department of Clinical and Experimental Medicine, Division of Rheumatology, University of Florence, AOU Careggi, Florence, Italy
| | - Yannick Allanore
- Department of Rheumatology A, Cochin Hospital and Cochin Institute, Assistance Publique-Hôpitaux de Paris, Paris-Descartes University, Paris, France
| | - Lázló Czirjak
- Department of Rheumatology and Immunology, University of Pécs, Pécs, Hungary
| | - Daniel E Furst
- Department of Rheumatology, David Geffen School of Medicine, University of California. Los Angeles, Los Angeles, CA
| | - Dinesh Khanna
- Department of Medicine, University of Michigan Scleroderma Program, University of Michigan, Ann Arbor, MI, USA
| | - Marco Matucci-Cerinic
- Department of Clinical and Experimental Medicine, Division of Rheumatology, University of Florence, AOU Careggi, Florence, Italy
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5
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Qi X, Ding L, Huang W, Wen B, Guo X, Zhang J. An improved automated type-based method for area assessment of wound surface. Wound Repair Regen 2017; 25:150-158. [PMID: 27859908 DOI: 10.1111/wrr.12495] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Accepted: 11/04/2016] [Indexed: 11/28/2022]
Abstract
Accurate and precise wound measurements are a critical component of wound detection and assessment. Digital cameras are convenient and objective tools that are being increasingly used worldwide to assist with wound measurements and assessments. However, heterogeneous wounds and poor lighting conditions continue to be obstacles to wound area recognition. This study, therefore, provides an improved automated type-based wound area assessment method that is robust to lighting conditions and can distinguish between different wound tissues based on wound colors. The results of both laboratory and clinical applications of the proposed method show excellent consistency of manual area measurements. This proposed technology is expected to provide wound care specialists with more clinical information about heterogeneous wounds, thereby enabling prospective cost savings for therapy and treatment.
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Affiliation(s)
- Xin Qi
- Department of Plastic Surgery & Burn, Peking University First Hospital, Beijing, People's Republic of China.,Diabetic Foot Treatment & Prevention Center, Peking University First Hospital, Beijing, People's Republic of China
| | - Lian Ding
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Wenjian Huang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Bing Wen
- Department of Plastic Surgery & Burn, Peking University First Hospital, Beijing, People's Republic of China.,Diabetic Foot Treatment & Prevention Center, Peking University First Hospital, Beijing, People's Republic of China
| | - Xiaohui Guo
- Diabetic Foot Treatment & Prevention Center, Peking University First Hospital, Beijing, People's Republic of China.,Department of Endocrinology, Peking University First Hospital, Beijing, People's Republic of China
| | - Jue Zhang
- Diabetic Foot Treatment & Prevention Center, Peking University First Hospital, Beijing, People's Republic of China.,Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China.,College of Engineering, Peking University, Beijing, People's Republic of China
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Scafide KN, Sheridan DJ, Taylor LA, Hayat MJ. Reliability of tristimulus colourimetry in the assessment of cutaneous bruise colour. Injury 2016; 47:1258-63. [PMID: 26997134 DOI: 10.1016/j.injury.2016.01.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 01/25/2016] [Indexed: 02/02/2023]
Abstract
BACKGROUND Bruising is one of the most common types of injury clinicians observe among victims of violence and other trauma patients. However, research has shown commonly used qualitative description of cutaneous bruise colour via the naked eye is subjective and unreliable. No published work has formally evaluated the reliability of tristimulus colourimetry as an alternative for assessing bruise colour, despite its clinical and research applications in accurately assessing skin colour. The purpose of this study was to systematically evaluate the test-retest and inter-observer reliability of tristimulus colourimetry in the assessment of cutaneous bruise colour. METHODS Two researchers obtained repeated tristimulus colourimetry measures of cutaneous bruises with participants of diverse skin colour. Measures were obtained using the Minolta CR-400 Chomameter. Commission Internationale d'Eclairage (CIE) L*a*b* colour space was used. Data was analysed using intraclass correlation coefficients (ICC), Cronbach's alpha, and minimal detectable change (MDC) on all three L*a*b* values. RESULTS The colorimeter demonstrated excellent test-retest or intra-rater reliability (L* ICC=0.999; a* ICC=0.973; b* ICC=0.892) and inter-rater reliability (L* ICC=0.997; a* ICC=0.976; b* ICC=0.982). CONCLUSIONS With consistent placement, the tristimulus colourimetry is reliable for the objective assessment and documentation of cutaneous bruise colour for purposes of clinical practice and research. Recommendations for use in practice/research are provided.
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Affiliation(s)
| | | | - Laura A Taylor
- Uniformed Services University of the Health Sciences, United States
| | - Matthew J Hayat
- Georgia State University, School of Public Health, United States
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7
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First results of a new hyperspectral camera system for chemical based wound analysis. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.wndm.2015.11.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Iizaka S, Koyanagi H, Sasaki S, Sekine R, Konya C, Sugama J, Sanada H. Nutrition-related status and granulation tissue colour of pressure ulcers evaluated by digital image analysis in older patients. J Wound Care 2014; 23:198-200, 202-6. [PMID: 24762383 DOI: 10.12968/jowc.2014.23.4.198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Granulation tissue colour may be an indicator for nutritional assessment in pressure ulcer (PU) care. This study evaluated the relationship between nutritional status, anaemia and diabetes, and granulation tissue colour of PUs by colour analysis of digital photographs in the clinical setting. METHOD The cross-sectional study included 42 older patients with 51 full-thickness PUs from 10 institutions. Patient demographics, wound status, nutritional status and dietary intakes were obtained from medical charts. From a wound image, the granulation red index was processed by computer software and the proportion of pixels exceeding the threshold intensity of 80 for the granulation tissue surface (%GRI80) was calculated. RESULTS Haemoglobin levels were positively associated with %GRI80 levels (p=0.007) in the crude model, but not in the adjusted model (p=0.260). The interaction term between diabetes and protein intake was significantly associated with %GRI80 levels in the adjusted models (p=0.010). At protein intakes of 0.95 g/kg or higher, diabetic wounds exhibited lower %GRI80 levels than non-diabetic wounds (p=0.002). At protein intakes of less than 0.95 g/kg, %GRI80 levels did not differ between diabetic and non-diabetic patients (p=0.247). Protein intakes of 0.95 g/kg or higher were associated with higher %GRI80 levels in non-diabetic patients (p=0.015), but not in diabetic patients (p=0.127). CONCLUSION Granulation tissue colour, evaluated by the objective and quantitative analysis of digital photography, is related to haemoglobin level, diabetes and dietary intakes in clinical settings.
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Affiliation(s)
- S Iizaka
- RN, PHN, PhD, Department of Gerontological Nursing/Wound Care Management, University of Tokyo, Tokyo, Japan
| | - H Koyanagi
- RN, MHS, University of Tokyo Hospital, Tokyo, Japan
| | - S Sasaki
- RN, University of Tokyo Hospital, Tokyo, Japan
| | - R Sekine
- RD, University of Tokyo Hospital, Tokyo, Japan
| | - C Konya
- RN, PhD, School of Nursing, Kanazawa Medical University, Ishikawa, Japan
| | - J Sugama
- RN, PhD, Graduate School of Medical Science, Kanazawa University, Ishikawa, Japan
| | - H Sanada
- RN, PhD, Department of Gerontological Nursing/Wound Care Management, University of Tokyo, Tokyo, Japan
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Minematsu T, Nakagami G, Yamamoto Y, Kanazawa T, Huang L, Koyanagi H, Sasaki S, Uchida G, Fujita H, Haga N, Yoshimura K, Nagase T, Sanada H. Wound blotting: A convenient biochemical assessment tool for protein components in exudate of chronic wounds. Wound Repair Regen 2013; 21:329-34. [DOI: 10.1111/wrr.12017] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Accepted: 11/27/2012] [Indexed: 11/28/2022]
Affiliation(s)
- Takeo Minematsu
- Department of Gerontological Nursing/Wound Care Management; Graduate School of Medicine; The University of Tokyo; Tokyo; Japan
| | - Gojiro Nakagami
- Department of Gerontological Nursing/Wound Care Management; Graduate School of Medicine; The University of Tokyo; Tokyo; Japan
| | - Yuko Yamamoto
- Department of Gerontological Nursing/Wound Care Management; Graduate School of Medicine; The University of Tokyo; Tokyo; Japan
| | - Toshiki Kanazawa
- Department of Gerontological Nursing/Wound Care Management; Graduate School of Medicine; The University of Tokyo; Tokyo; Japan
| | - Lijuan Huang
- Department of Gerontological Nursing/Wound Care Management; Graduate School of Medicine; The University of Tokyo; Tokyo; Japan
| | - Hiroe Koyanagi
- Department of Nursing; The University of Tokyo Hospital; Tokyo; Japan
| | - Sanae Sasaki
- Department of Nursing; The University of Tokyo Hospital; Tokyo; Japan
| | - Gentaro Uchida
- Department of Plastic Surgery; The University of Tokyo; Tokyo; Japan
| | - Hideki Fujita
- Department of Dermatology; The University of Tokyo; Tokyo; Japan
| | - Nobuhiko Haga
- Department of Rehabilitation Medicine; The University of Tokyo; Tokyo; Japan
| | - Kotaro Yoshimura
- Department of Plastic Surgery; The University of Tokyo; Tokyo; Japan
| | - Takashi Nagase
- Department of Gerontological Nursing/Wound Care Management; Graduate School of Medicine; The University of Tokyo; Tokyo; Japan
| | - Hiromi Sanada
- Department of Gerontological Nursing/Wound Care Management; Graduate School of Medicine; The University of Tokyo; Tokyo; Japan
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Iizaka S, Kaitani T, Sugama J, Nakagami G, Naito A, Koyanagi H, Konya C, Sanada H. Predictive validity of granulation tissue color measured by digital image analysis for deep pressure ulcer healing: a multicenter prospective cohort study. Wound Repair Regen 2012; 21:25-34. [DOI: 10.1111/j.1524-475x.2012.00841.x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2012] [Accepted: 07/23/2012] [Indexed: 12/31/2022]
Affiliation(s)
- Shinji Iizaka
- Department of Gerontological Nursing/Wound Care Management; Division of Health Sciences and Nursing; Graduate School of Medicine; The University of Tokyo; Tokyo Japan
| | - Toshiko Kaitani
- Department of Gerontological Nursing/Wound Care Management; Division of Health Sciences and Nursing; Graduate School of Medicine; The University of Tokyo; Tokyo Japan
| | - Junko Sugama
- Division of Health Sciences, Graduate School of Medical Science; Kanazawa University; Ishikawa Japan
| | - Gojiro Nakagami
- Department of Gerontological Nursing/Wound Care Management; Division of Health Sciences and Nursing; Graduate School of Medicine; The University of Tokyo; Tokyo Japan
| | | | | | - Chizuko Konya
- School of Nursing; Kanazawa Medical University; Ishikawa Japan
| | - Hiromi Sanada
- Department of Gerontological Nursing/Wound Care Management; Division of Health Sciences and Nursing; Graduate School of Medicine; The University of Tokyo; Tokyo Japan
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