1
|
Mert M, Vahabi A, Daştan AE, Kuyucu A, Ünal YC, Tezgel O, Öztürk AM, Taşbakan M, Aktuğlu K. Artificial intelligence's suggestions for level of amputation in diabetic foot ulcers are highly correlated with those of clinicians, only with exception of hindfoot amputations. Int Wound J 2024; 21:e70055. [PMID: 39353602 PMCID: PMC11444738 DOI: 10.1111/iwj.70055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 09/01/2024] [Accepted: 09/02/2024] [Indexed: 10/04/2024] Open
Abstract
Diabetic foot ulcers (DFUs) are a growing public health problem, paralleling the increasing incidence of diabetes. While prevention is most effective treatment for DFUs, challenge remains on selecting the optimal treatment in cases with DFUs. Health sciences have greatly benefited from the integration of artificial intelligence (AI) applications across various fields. Regarding amputations in DFUs, both literature and clinical practice have mainly focused on strategies to prevent amputation and identify avoidable risk factor. However, there are very limited data on assistive parameters/tools that can be used to determine the level of amputation. This study investigated how well ChatGPT, with its lately released version 4o, matches the amputation level selection of an experienced team in this field. For this purpose, clinical photographs from patients who underwent amputations due to diabetic foot ulcers between May 2023 and May 2024 were submitted to the ChatGPT-4o program. The AI was tasked with recommending an appropriate amputation level based on these clinical photographs. Data from a total of 60 patients were analysed, with a median age of 64.5 years (range: 41-91). According to the Wagner Classification, 32 patients (53.3%) had grade 4 ulcers, 16 patients (26.6%) had grade 5 ulcers, 10 patients (16.6%) had grade 3 ulcers and 2 patients (3.3%) had grade 2 ulcers. A one-to-one correspondence between the AI tool's recommended amputation level and the level actually performed was observed in 50 out of 60 cases (83.3%). In the remaining 10 cases, discrepancies were noted, with the AI consistently recommending a more proximal level of amputation than what was performed. The inter-rater agreement analysis between the actual surgeries and the AI tool's recommendations yielded a Cohen's kappa coefficient of 0.808 (SD: 0.055, 95% CI: 0.701-0.916), indicating substantial agreement. Relying solely on clinical photographs, ChatGPT-4.0 demonstrates decisions that are largely consistent with those of an experienced team in determining the optimal level of amputation for DFUs, with the exception of hindfoot amputations.
Collapse
Affiliation(s)
- Merve Mert
- Department of Orthopedics and TraumatologyEge University School of MedicineIzmirTurkey
- Department of Infectious Diseases and Clinical MicrobiologyEge University School of MedicineIzmirTurkey
| | - Arman Vahabi
- Department of Orthopedics and TraumatologyEge University School of MedicineIzmirTurkey
| | - Ali Engin Daştan
- Department of Orthopedics and TraumatologyEge University School of MedicineIzmirTurkey
| | - Abdussamet Kuyucu
- Department of Orthopedics and TraumatologyEge University School of MedicineIzmirTurkey
- Department of Infectious Diseases and Clinical MicrobiologyEge University School of MedicineIzmirTurkey
| | - Yunus Can Ünal
- Department of Orthopaedics and TraumatologyVan Educational and Research HospitalVanTurkey
| | - Okan Tezgel
- Department of Orthopaedics and TraumatologyVan Educational and Research HospitalVanTurkey
| | - Anıl Murat Öztürk
- Department of Orthopedics and TraumatologyEge University School of MedicineIzmirTurkey
| | - Meltem Taşbakan
- Department of Infectious Diseases and Clinical MicrobiologyEge University School of MedicineIzmirTurkey
| | - Kemal Aktuğlu
- Department of Orthopedics and TraumatologyEge University School of MedicineIzmirTurkey
| |
Collapse
|
2
|
Gagnon J, Probst S, Chartrand J, Reynolds E, Lalonde M. Self-supporting wound care mobile applications for nurses: A scoping review. J Adv Nurs 2024; 80:3464-3480. [PMID: 38186080 DOI: 10.1111/jan.16052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/11/2023] [Accepted: 12/23/2023] [Indexed: 01/09/2024]
Abstract
AIM This study provides an overview of the literature to identify and map the types of available evidence on self-supporting mobile applications used by nurses in wound care regarding their development, evaluation and outcomes for patients, nurses and the healthcare system. DESIGN Scoping review. REVIEW METHOD Joanna Briggs Institute scoping review methodology was used. DATA SOURCES A search was performed using MEDLINE, Embase, CINAHL (via EBSCO), Web of Science, LiSSa (Littérature Scientifique en Santé), Cochrane Wounds, Érudit and grey literature, between April and October 2022, updated in April 2023, to identify literature published in English and French. RESULTS Eleven studies from 14 publications met the inclusion criteria. Mostly descriptive, the included studies presented mobile applications that nurses used, among other things, to assess wounds and support clinical decision-making. The results described how nurses were iteratively involved in the process of developing and evaluating mobile applications using various methods such as pilot tests. The three outcomes most frequently reported by nurses were as follows: facilitating care, documentation on file and access to evidence-based data. CONCLUSION The potential of mobile applications in wound care is within reach. Nurses are an indispensable player in the successful development of these tools. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE If properly developed and evaluated, mobile applications for wound care could enhance nursing practices and improve patient care. The development of ethical digital competence must be ensured during initial training and continued throughout the professional journey. IMPACT We identified a dearth of studies investigating applications that work without Internet access. More research is needed on the development of mobile applications in wound care and their possible impact on nursing practice in rural areas and the next generation of nurses. REPORTING METHOD The Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Review guidelines were used. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.
Collapse
Affiliation(s)
- Julie Gagnon
- School of Nursing, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
- Département des sciences de la santé, Université du Québec à Rimouski, Rimouski, Québec, Canada
| | - Sebastian Probst
- HES-SO, University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland
- Care Directorate, University Hospital Geneva, Geneva, Switzerland
- Faculty of Medicine Nursing and Health Sciences, University of Galway, Galway, Ireland
| | - Julie Chartrand
- School of Nursing, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Emily Reynolds
- School of Nursing, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
| | - Michelle Lalonde
- School of Nursing, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
- Institut du Savoir Montfort, Montfort Hospital, Ottawa, Ontario, Canada
| |
Collapse
|
3
|
Li X, Chen D, Wang C, Fan J, Wang Z, Liu Y, Wang W, Kong C. Research hotspots and trends in nursing for diabetic foot ulcers: A bibliometric analysis from 2013 to 2023. Heliyon 2024; 10:e36009. [PMID: 39224296 PMCID: PMC11367126 DOI: 10.1016/j.heliyon.2024.e36009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 08/01/2024] [Accepted: 08/07/2024] [Indexed: 09/04/2024] Open
Abstract
Background Nursing can effectively prevent and ameliorate diabetic foot ulcers (DFU). However, there is a lack of literature on the bibliometric analysis of DFU nursing. This study aimed to analyze the research hotspots and development trends in DFU nursing over the past 10 years to provide references for future related research. Methods The Web of Science Core Collection was used to retrieve literature related to DFU nursing from 2013 to 2023. Analyses included the annual publication trends; author, institution, and country collaborations; journal and literature co-citation; and keyword co-occurrence, clustering, and bursting, performed using CiteSpace 5.8 R3. Results A total of 229 papers were included, showing an upward trend in annual publications. American scholar David G Armstrong (n = 3) and King's College Hospital London (n = 4) were the most productive authors and institutions, respectively. The United States ranked first (n = 45) in national contributions, followed by China and Brazil. The overall research strength between authors and institutions was relatively scattered, and intensive cooperation has not yet been formed. National collaborations resulted in a core team dominated by Europe and North America with concentrated research strengths. The most frequently co-cited journal and co-cited reference were Diabetes Care (111 citations) and Armstrong DG (2017) (131 citations), separately. Research hotspots mainly focused on risk assessment, classification systems, protective measures, and clinical management of DFU. "Primary care" and "intervention efficacy" were identified as the research trends in the coming years. Conclusion The field of DFU nursing requires more attention. Academic exchange and cooperation between authors, institutions, and countries should be strengthened. Our future research will focus on the latest hotspots and trends, conducting more in-depth and comprehensive studies on DFU management.
Collapse
Affiliation(s)
- Xiaoyun Li
- School of Nursing, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Dongfeng Chen
- First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Chen Wang
- First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Jingna Fan
- College of Integrative Chinese and Western Medicine, Jining Medical University, Jining, 272067, China
| | - Zhixin Wang
- First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yingjun Liu
- First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Wenkuan Wang
- First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Chang Kong
- School of Nursing, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| |
Collapse
|
4
|
Kabir MA, Samad S, Ahmed F, Naher S, Featherston J, Laird C, Ahmed S. Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities. J Med Syst 2024; 48:80. [PMID: 39180710 PMCID: PMC11344716 DOI: 10.1007/s10916-024-02091-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 07/22/2024] [Indexed: 08/26/2024]
Abstract
With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.
Collapse
Affiliation(s)
- Muhammad Ashad Kabir
- School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, 2795, NSW, Australia.
| | - Sabiha Samad
- Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Chattogram, Bangladesh
| | - Fahmida Ahmed
- Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Chattogram, Bangladesh
| | - Samsun Naher
- Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Chattogram, Bangladesh
| | - Jill Featherston
- School of Medicine, Cardiff University, Cardiff, CF14 4YS, Wales, United Kingdom
| | - Craig Laird
- Principal Pedorthist, Walk Easy Pedorthics Pty. Ltd., Tamworth, 2340, NSW, Australia
| | - Sayed Ahmed
- Principal Pedorthist, Foot Balance Technology Pty Ltd, Westmead, 2145, NSW, Australia
- Offloading Clinic, Nepean Hospital, Kingswood, 2750, NSW, Australia
| |
Collapse
|
5
|
Weatherall T, Avsar P, Nugent L, Moore Z, McDermott JH, Sreenan S, Wilson H, McEvoy NL, Derwin R, Chadwick P, Patton D. The impact of machine learning on the prediction of diabetic foot ulcers - A systematic review. J Tissue Viability 2024:S0965-206X(24)00109-8. [PMID: 39019690 DOI: 10.1016/j.jtv.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 07/19/2024]
Abstract
INTRODUCTION Globally, diabetes mellitus poses a significant health challenge as well as the associated complications of diabetes, such as diabetic foot ulcers (DFUs). The early detection of DFUs is important in the healing process and machine learning may be able to help inform clinical staff during the treatment process. METHODS A PRISMA-informed search of the literature was completed via the Cochrane Library and MEDLINE (OVID), EMBASE, CINAHL Plus and Scopus databases for reports published in English and in the last ten years. The primary outcome of interest was the impact of machine learning on the prediction of DFUs. The secondary outcome was the statistical performance measures reported. Data were extracted using a predesigned data extraction tool. Quality appraisal was undertaken using the evidence-based librarianship critical appraisal tool. RESULTS A total of 18 reports met the inclusion criteria. Nine reports proposed models to identify two classes, either healthy skin or a DFU. Nine reports proposed models to predict the progress of DFUs, for example, classing infection versus non-infection, or using wound characteristics to predict healing. A variety of machine learning techniques were proposed. Where reported, sensitivity = 74.53-98 %, accuracy = 64.6-99.32 %, precision = 62.9-99 %, and the F-measure = 52.05-99.0 %. CONCLUSIONS A variety of machine learning models were suggested to successfully classify DFUs from healthy skin, or to inform the prediction of DFUs. The proposed machine learning models may have the potential to inform the clinical practice of managing DFUs and may help to improve outcomes for individuals with DFUs. Future research may benefit from the development of a standard device and algorithm that detects, diagnoses and predicts the progress of DFUs.
Collapse
Affiliation(s)
- Teagan Weatherall
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Pinar Avsar
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Linda Nugent
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia.
| | - Zena Moore
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia; School of Nursing and Midwifery, Griffith University, Southport, Queensland, Australia; Lida Institute, Shanghai, China; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia; Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; University of Wales, Cardiff, UK; National Health and Medical Research Council Centre of Research Excellence in Wiser Wound Care, Menzies Health Institute Queensland, Southport, Queensland, Australia.
| | - John H McDermott
- Department of Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital Blanchardstown, Dublin, Ireland.
| | - Seamus Sreenan
- Department of Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital Blanchardstown, Dublin, Ireland.
| | - Hannah Wilson
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Natalie L McEvoy
- School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Rosemarie Derwin
- School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Paul Chadwick
- Birmingham City University, Birmingham, UK; Spectral MD, London, UK.
| | - Declan Patton
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia; School of Nursing and Midwifery, Griffith University, Southport, Queensland, Australia; Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, New South Wales, Australia.
| |
Collapse
|
6
|
Taneja S, Barbee DL, Cohen RF, Malin M. Implementation of a Stereoscopic Camera System for Clinical Electron Simulation and Treatment Planning. Pract Radiat Oncol 2024; 14:e291-e300. [PMID: 38325547 DOI: 10.1016/j.prro.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/14/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE A 3-dimensinal (3D) stereoscopic camera system developed by .decimal was commissioned and implemented into the clinic to improve the efficiency of clinical electron simulations. Capabilities of the camera allowed simulations to be moved from the treatment vault into any room with a flat surface that could accommodate patient positioning devices, eliminating the need for clinical patient setup timeslots on the treatment machine. This work describes the process used for these simulations and compares the treatment parameters determined by the system to those used in delivery. METHODS AND MATERIALS The Decimal3D scanner workflow consisted of: scanning the patient surface; contouring the treatment area; determining gantry, couch, collimator, and source-to-surface distance (SSD) parameters for en face entry of the beam with sufficient clearance at the machine; and ordering custom electron cutouts when needed. Transparencies showing the projection of in-house library cutouts at various clinical SSDs were created to assist in choosing an appropriate library cutout. Data from 73 treatment sites were analyzed to evaluate the accuracy of the scanner-determined beam parameters for each treatment delivery. RESULTS Clinical electron simulations for 73 treatment sites, predominately keloids, were transitioned out of the linear accelerator (LINAC) vault using the new workflow. For all patients, gantry, collimator, and couch parameters, along with SSD and cone size, were determined using the Decimal3D scanner with 57% of simulations using library cutouts. Tolerance tables for patient setup were updated to allow differences of 10, 20, and 5° for gantry, collimator, and couch, respectively. Approximately 7% of fractions (N = 181 total fractions) were set up outside of the tolerance table based on physician direction during treatment. This reflects physician preference to adjust the LINAC rather than patient position during treatment setup. No scanner-derived plan was untreatable because of cutout shape inaccuracy or clearance issues. CONCLUSIONS Clinical electron simulations were successfully transitioned out of the LINAC vault using the Decimal3D scanner without loss of setup accuracy, as measured through machine parameter determination and electron cutout shape.
Collapse
Affiliation(s)
- Sameer Taneja
- Department of Radiation Oncology, New York University Langone Medical Center, New York, New York.
| | - David L Barbee
- Department of Radiation Oncology, New York University Langone Medical Center, New York, New York
| | - Richard F Cohen
- Department of Radiation Oncology, New York University Langone Medical Center, New York, New York
| | - Martha Malin
- Department of Radiation Oncology, New York University Langone Medical Center, New York, New York
| |
Collapse
|
7
|
Rippon MG, Fleming L, Chen T, Rogers AA, Ousey K. Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review. J Wound Care 2024; 33:229-242. [PMID: 38573907 DOI: 10.12968/jowc.2024.33.4.229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
OBJECTIVE The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.
Collapse
Affiliation(s)
- Mark G Rippon
- University of Huddersfield, Huddersfield, UK
- Daneriver Consultancy Ltd, Holmes Chapel, UK
| | - Leigh Fleming
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | - Tianhua Chen
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | | | - Karen Ousey
- University of Huddersfield Department of Nursing and Midwifery, Huddersfield, UK
- Adjunct Professor, School of Nursing, Faculty of Health at the Queensland University of Technology, Australia
- Visiting Professor, Royal College of Surgeons in Ireland, Dublin, Ireland
- Chair, International Wound Infection Institute
- President Elect, International Skin Tear Advisory Panel
| |
Collapse
|
8
|
Lo ZJ, Mak MHW, Liang S, Chan YM, Goh CC, Lai T, Tan A, Thng P, Rodriguez J, Weyde T, Smit S. Development of an explainable artificial intelligence model for Asian vascular wound images. Int Wound J 2024; 21:e14565. [PMID: 38146127 PMCID: PMC10961881 DOI: 10.1111/iwj.14565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 12/04/2023] [Indexed: 12/27/2023] Open
Abstract
Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.
Collapse
Affiliation(s)
- Zhiwen Joseph Lo
- Department of SurgeryWoodlands HealthSingaporeSingapore
- Lee Kong Chian School of MedicineNanyang Technological UniversitySingaporeSingapore
| | | | | | - Yam Meng Chan
- Department of General SurgeryTan Tock Seng HospitalSingaporeSingapore
| | - Cheng Cheng Goh
- Wound and Stoma Care, Nursing SpecialityTan Tock Seng HospitalSingaporeSingapore
| | - Tina Lai
- Wound and Stoma Care, Nursing SpecialityTan Tock Seng HospitalSingaporeSingapore
| | - Audrey Tan
- Wound and Stoma Care, Nursing SpecialityTan Tock Seng HospitalSingaporeSingapore
| | - Patrick Thng
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
| | - Jorge Rodriguez
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
| | - Tillman Weyde
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
| | - Sylvia Smit
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
| |
Collapse
|
9
|
Wu Y, Wu L, Yu M. The clinical value of intelligent wound measurement devices in patients with chronic wounds: A scoping review. Int Wound J 2024; 21:e14843. [PMID: 38494195 PMCID: PMC10944690 DOI: 10.1111/iwj.14843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/19/2024] Open
Abstract
Chronic wounds are common in clinical practice, with long treatment cycle and high treatment cost. Changes in wound area can well predict the effectiveness of treatment and the possibility of healing. Therefore, continuous wound monitoring and evaluation are particularly important. Traditional manual wound measurement tends to overestimate wound area. Recently, various intelligent wound measurement devices have been introduced into clinical practice. This review aims to summarise the reliability, validity, types and measurement principles of different intelligent wound measurement devices, so as to analyse the clinical value and application prospect. Articles numbering 2610 were retrieved from the database, and 14 articles met the inclusion criteria. The results showed that the intelligent wound measurement devices included in the study reported good reliability and validity. Contact devices can lead to wound bed damage, wound deformation, patient pain, and is not convenient for electronic wound recording; partial contact devices can complete continuous monitoring and recording of wounds, but are not sensitive to wound depth measurement. Non-contact devices are more accurate in capturing wound images. In addition to wound measurement, they also have the function of wound assessment. In general, handheld and portable non-contact devices have great clinical value and promotion prospects.
Collapse
Affiliation(s)
- Yujie Wu
- Department of Nursing, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Liping Wu
- Department of NursingChildren's Hospital of Chongqing Medical UniversityChongqingChina
| | - Mingfeng Yu
- Department of Nursing, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| |
Collapse
|
10
|
Ng CE, Bowman S, Ling J, Bagshaw R, Birt A, Yiannakou Y. The future of clinical trials-is it virtual? Br Med Bull 2023; 148:42-57. [PMID: 37681298 DOI: 10.1093/bmb/ldad022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/10/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023]
Abstract
INTRODUCTION Participant recruitment to clinical trials is often sub-optimal. Decentralized clinical trials have the potential to address challenges in traditional site-based clinical trial recruitment. SOURCES OF DATA This review is based on recently published literature and the experience of running a large industry-sponsored interventional trial using both traditional and decentralized methods. AREAS OF AGREEMENT Efficient delivery of clinical trials is essential to continue to provide therapeutic improvements in a timely and cost-efficient way. Clinical trial designs are constantly evolving to achieve effective trial delivery, manage the complexity of new therapeutic algorithms and conform to cultural developments. AREAS OF CONTROVERSY Digitally innovative decentralized clinical trials may be a solution to improve recruitment and retention. Although many trials incorporate digital innovations to reduce patient burden, decentralized clinical trials allow remote access to clinical research, potentially enhancing geographical diversity as well as reducing participant burden. GROWING POINTS Areas for development currently being discussed are developing a 'recruitment platform' that exploits the reach of digital connectivity, automated identification of eligible participants from volunteers, employing technology for remote interaction and exploring the logistic process of delivering the interventions. AREAS TIMELY FOR RELEVANT RESEARCH The focus of development must ensure that the overall impact will widen participation and reduce inequalities in healthcare.
Collapse
Affiliation(s)
- Cho Ee Ng
- Durham Bowel Service, County Durham and Darlington NHS Foundation Trust, Durham, DH1 5TW, UK
- NIHR Patient Recruitment Centre, Newcastle, NE4 6BE, UK
| | - Sarah Bowman
- Department of Arts, Design and Social Sciences, Northumbria University, Newcastle, NE1 8ST, UK
| | | | - Rachael Bagshaw
- Just R Ltd, Specialists in Marketing, Brand and Communications, Carlisle, CA3 8RY, UK
| | - Angela Birt
- NIHR Patient Recruitment Centre, Newcastle, NE4 6BE, UK
| | - Yan Yiannakou
- Durham Bowel Service, County Durham and Darlington NHS Foundation Trust, Durham, DH1 5TW, UK
- NIHR Patient Recruitment Centre, Newcastle, NE4 6BE, UK
| |
Collapse
|
11
|
Kim D, Jeong J, Kim J, Cho Y, Park I, Lee SM, Oh YT, Baek S, Kang D, Lee E, Jeong B. Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians. J Korean Med Sci 2023; 38:e322. [PMID: 37987103 PMCID: PMC10659922 DOI: 10.3346/jkms.2023.38.e322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/22/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. METHODS We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). RESULTS Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application's output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss' kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss' kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians' consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients' sex and age (P < 0.001 for both). CONCLUSION Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.
Collapse
Affiliation(s)
- Donghoon Kim
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Joo Jeong
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Joonghee Kim
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Division of Data Science, Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea
- ARPI Inc., Seongnam, Korea.
| | - Youngjin Cho
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- ARPI Inc., Seongnam, Korea
| | - Inwon Park
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sang-Min Lee
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Young Taeck Oh
- Department of Emergency Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Sumin Baek
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Division of Data Science, Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Dongin Kang
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | | | | |
Collapse
|
12
|
Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers. J Clin Med 2023; 12:jcm12062194. [PMID: 36983198 PMCID: PMC10057569 DOI: 10.3390/jcm12062194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/04/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Artificial intelligence (AI) in medical care can raise diagnosis accuracy and improve its uniformity. This study developed a diagnostic imaging system for chronic wounds that can be used in medically underpopulated areas. The image identification algorithm searches for patterns and makes decisions based on information obtained from pixels rather than images. Images of 50 patients with pressure sores treated at Kobe University Hospital were examined. The algorithm determined the presence of necrosis with a significant difference (p = 3.39 × 10−5). A threshold value was created with a luminance difference of 50 for the group with necrosis of 5% or more black pixels. In the no-necrosis group with less than 5% black pixels, the threshold value was created with a brightness difference of 100. The “shallow wounds” were distributed below 100, whereas the “deep wounds” were distributed above 100. When the algorithm was applied to 24 images of 23 new cases, there was 100% agreement between the specialist and the algorithm regarding the presence of necrotic tissue and wound depth evaluation. The algorithm identifies the necrotic tissue and wound depth without requiring a large amount of data, making it suitable for application to future AI diagnosis systems for chronic wounds.
Collapse
|
13
|
Rodrigues CF, Bezerra SMG, Calçada DB. COMPUTER SYSTEMS TO AID IN WOUND HEALING: SCOPE REVIEW. ESTIMA 2023. [DOI: 10.30886/estima.v21.1260_in] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
Objective: To investigate studies that present computational systems to aid healing and systems which refer to the use of low-level laser.Method: Scope review that aimed to answer the question: Which computer systems help in wound healing? A subquestion was: Which of the computer systems refer to the use of low-level laser? Results: From the search, applying the eligibility criteria, 49 articles made up the final sample. The systems served multiple purposes in support of wound healing; the majority presented the health professional as a user of the system; medicine was the most mentioned professional area despite nursing being involved in the management of care for people with wounds. Innovation in care using the computer system was frequently reported, demonstrating the importance of this type of tool for clinical practice. There was a high frequency of the mobile platform, showing that this is a current trend. Conclusion:Computer systems have been used as tools to support patients and especially professionals in wound healing. Regarding the systems aimed at the low intensity laser, there was a shortage of computer systems for this purpose, with a study.
Collapse
|
14
|
Fong KY, Lai TP, Chan KS, See IJL, Goh CC, Muthuveerappa S, Tan AH, Liang S, Lo ZJ. Clinical validation of a smartphone application for automated wound measurement in patients with venous leg ulcers. Int Wound J 2023; 20:751-760. [PMID: 36787270 PMCID: PMC9927911 DOI: 10.1111/iwj.13918] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/24/2022] [Accepted: 07/25/2022] [Indexed: 12/07/2022] Open
Abstract
Chronic wounds are associated with significant clinical, economic and quality-of-life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5-week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter-rater and intra-rater reliability between clinical and TA-based measurements were analysed using intra-class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5-week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter-rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799-0.919, P < 0.001). Separate measurements of intra-rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967-0.985 and range 0.977-0.984 respectively, P < 0.001). Inter-rater reliability between TA used on the iOS and Android systems was also excellent (0.987-0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity.
Collapse
Affiliation(s)
- Khi Yung Fong
- Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
| | - Tina Peiting Lai
- Wound and Stoma Care, Nursing ServiceTan Tock Seng HospitalSingaporeSingapore
| | - Kai Siang Chan
- Vascular Surgery Service, Department of General SurgeryTan Tock Seng HospitalSingaporeSingapore
| | - Isabel Jia Le See
- Vascular Surgery Service, Department of General SurgeryTan Tock Seng HospitalSingaporeSingapore
| | - Cheng Cheng Goh
- Wound and Stoma Care, Nursing ServiceTan Tock Seng HospitalSingaporeSingapore
| | | | - Audrey Huimin Tan
- Wound and Stoma Care, Nursing ServiceTan Tock Seng HospitalSingaporeSingapore
| | | | - Zhiwen Joseph Lo
- Department of SurgeryWoodlands HealthSingaporeSingapore
- Lee Kong Chian School of MedicineNanyang Technological UniversitySingaporeSingapore
| |
Collapse
|
15
|
Rodrigues CF, Bezerra SMG, Calçada DB. SISTEMAS COMPUTACIONAIS PARA AUXÍLIO NA CICATRIZAÇÃO DE FERIDAS: REVISÃO DE ESCOPO. ESTIMA 2023. [DOI: 10.30886/estima.v21.1260_pt] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
Objetivo:Investigar estudos que apresentem sistemas computacionais de auxílio à cicatrização de feridas e quais sistemas se referem ao uso de laser de baixa intensidade. Método: Revisão de escopo que visou responder à questão de pesquisa: Quais sistemas computacionais auxiliam na cicatrização de feridas? Uma subquestão foi: quais sistemas computacionais se referem ao uso do laser de baixa intensidade? Resultados: A partir da busca, aplicando os critérios de elegibilidade, 49 artigos compuseram a amostra final. Os sistemas apresentaram várias finalidades de apoio à cicatrização de feridas, em que a maioria apresentou como usuário do sistema o profissional de saúde, sendo a medicina a área profissional mais mencionada, embora a enfermagem esteja envolvida com o manejo do cuidado às pessoas com feridas. Foi relatada com frequência a inovação na assistência a partir do uso do sistema computacional, o que demonstra a importância desse tipo de ferramenta para a prática clínica. Verificou-se com frequência o uso de plataforma mobile, como tendência da atualidade. Conclusão: Os sistemas computacionais têm sido utilizados como ferramentas para apoiar pacientes e principalmente profissionais na cicatrização de feridas. Quanto ao laser de baixa intensidade, houve escassez de sistemas computacionais com essa finalidade, com apenas um estudo.
Collapse
|
16
|
Chairat S, Chaichulee S, Dissaneewate T, Wangkulangkul P, Kongpanichakul L. AI-Assisted Assessment of Wound Tissue with Automatic Color and Measurement Calibration on Images Taken with a Smartphone. Healthcare (Basel) 2023; 11:healthcare11020273. [PMID: 36673641 PMCID: PMC9858639 DOI: 10.3390/healthcare11020273] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 01/17/2023] Open
Abstract
Wound assessment is essential for evaluating wound healing. One cornerstone of wound care practice is the use of clinical guidelines that mandate regular documentation, including wound size and wound tissue composition, to determine the rate of wound healing. The traditional method requires wound care professionals to manually measure the wound area and tissue composition, which is time-consuming, costly, and difficult to reproduce. In this work, we propose an approach for automatic wound assessment that incorporates automatic color and measurement calibration and artificial intelligence algorithms. Our approach enables the comparison of images taken at different times, even if they were taken under different lighting conditions, distances, lenses, and camera sensors. We designed a calibration chart and developed automatic algorithms for color and measurement calibration. The wound area and wound composition on the images were annotated by three physicians with more than ten years of experience. Deep learning models were then developed to mimic what the physicians did on the images. We examined two network variants, U-Net with EfficientNet and U-Net with MobileNetV2, on wound images with a size of 1024 × 1024 pixels. Our best-performing algorithm achieved a mean intersection over union (IoU) of 0.6964, 0.3957, 0.6421, and 0.1552 for segmenting a wound area, epithelialization area, granulation tissue, and necrotic tissue, respectively. Our approach was able to accurately segment the wound area and granulation tissue but was inconsistent with respect to the epithelialization area and necrotic tissue. The calibration chart, which helps calibrate colors and scales, improved the performance of the algorithm. The approach could provide a thorough assessment of the wound, which could help clinicians tailor treatment to the patient's condition.
Collapse
Affiliation(s)
- Sawrawit Chairat
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Sitthichok Chaichulee
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
- Research Center for Medical Data Analytics, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Tulaya Dissaneewate
- Department of Rehabilitation Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Piyanun Wangkulangkul
- Division of General Surgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Laliphat Kongpanichakul
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
- Correspondence:
| |
Collapse
|
17
|
Machado MAD, Silva RRE, Namias M, Lessa AS, Neves MCLC, Silva CTA, Oliveira DM, Reina TR, Lira AAB, Almeida LM, Zanchettin C, Netto EM. Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography. J Med Biol Eng 2023; 43:156-162. [PMID: 37077697 PMCID: PMC9990550 DOI: 10.1007/s40846-023-00781-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/16/2023] [Indexed: 04/21/2023]
Abstract
Purpose To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Results Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.
Collapse
Affiliation(s)
- Marcos A. D. Machado
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
| | - Ronnyldo R. E. Silva
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
- Department of Systems and Computing, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58429-900 Brazil
| | - Mauro Namias
- Department of Medical Physics, Nuclear Diagnostic Center Foundation, C1417CVE Buenos Aires, Argentina
| | - Andreia S. Lessa
- Department of Radiology, Hospital Universitário Gaffrée e Guinle, Universidade do Rio de Janeiro (UNIRIO), Rio de Janeiro, 20270-004 Brazil
| | - Margarida C. L. C. Neves
- Department of Pneumology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Carolina T. A. Silva
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Danillo M. Oliveira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
- Northeast Regional Nuclear Science Centre (CRCN-NE), Recife, Pernambuco 50840-545 Brazil
- Nuclear Energy Department, Universidade Federal de Pernambuco, Recife, Pernambuco 50740-540 Brazil
| | - Thamiris R. Reina
- Department of Radiology, Hospital Universitário da Universidade Federal de Juiz de Fora/ Ebserh, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais 36038-330 Brazil
| | - Arquimedes A. B. Lira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
| | - Leandro M. Almeida
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
| | - Cleber Zanchettin
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208 USA
| | - Eduardo M. Netto
- Infectious Disease Research Laboratory, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| |
Collapse
|
18
|
Lo ZJ, Harish KB, Tan E, Zhu J, Chan S, Liew H, Hoi WH, Liang S, Cho YT, Koo HY, Wu K, Car J. A feasibility study on the efficacy of a patient-owned wound surveillance system for diabetic foot ulcer care (ePOWS study). Digit Health 2023; 9:20552076231205747. [PMID: 37808235 PMCID: PMC10559723 DOI: 10.1177/20552076231205747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Objective Wound image analysis tools hold promise in helping patients to monitor their wounds. We aim to perform a novel feasibility study on the efficacy of a patient-owned wound surveillance system for diabetic foot ulcer (DFU) care. Methods This two-institutional, prospective, single-arm pilot study examined patients with DFU. An artificial intelligence-enabled image analysis app calculating the wound surface area was installed and patients or caregivers were instructed to take pictures of wounds during dressing changes. Patients were followed until wound deterioration, wound healing, or wound stability at 6 months occurred and the outcomes of interest included study adherence, algorithm performance, and user experience. Results Between January 2021 and December 2021, 39 patients were enrolled in the study, with a mean age of 61.6 ± 8.6 years, and 69% (n = 27) of subjects were male. All patients had documented diabetes and 85% (n = 33) of them had peripheral arterial disease. A mean follow-up for those completing the study was 12.0 ± 8.5 weeks. At the conclusion of the study, 80% of patients (n = 20) had primary wound healing whilst 20% (n = 5) had wound deterioration. The study completion rate was 64% (n = 25). Usage of the app for surveillance of DFU healing, as compared to physician evaluation, yielded a sensitivity of 100%, specificity of 20%, positive predictive value of 83%, and negative predictive value of 100%. Of those who provided user experience feedback, 59% (n = 10) felt the app was easy to use, 47% (n = 8) would recommend the wound analysis app to others but only 6% would pay for the app out of pocket (n = 1). Conclusion Implementation of a patient-owned wound surveillance system is feasible. Most patients were able to effectively monitor wounds using a smartphone app-based solution. The image analysis algorithm demonstrates strong performance in identifying wound healing and is capable of detecting deterioration prior to interval evaluation by a physician. Patients generally found the app easy to use but were reluctant to pay for the use of the solution out of pocket.
Collapse
Affiliation(s)
- Zhiwen J Lo
- Department of Surgery, Vascular Surgery Service, Woodlands Health, Singapore, Singapore
- Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | | | - Elaine Tan
- National Healthcare Group Polyclinics, Singapore, Singapore
| | - Julia Zhu
- National Healthcare Group Polyclinics, Singapore, Singapore
| | - Shaun Chan
- Department of General Surgery, Vascular Surgery Service, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Huiling Liew
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore
| | - Wai H Hoi
- Department of Endocrinology, Woodlands Health, Singapore, Singapore
| | - Shanying Liang
- Department of Surgery, Vascular Surgery Service, Woodlands Health, Singapore, Singapore
| | - Yuan T Cho
- Department of Surgery, Vascular Surgery Service, Woodlands Health, Singapore, Singapore
| | - Hui Y Koo
- Group Integrated Care, National Healthcare Group, Singapore, Singapore
| | | | - Josip Car
- Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK
| |
Collapse
|
19
|
Chemello G, Salvatori B, Morettini M, Tura A. Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review. BIOSENSORS 2022; 12:985. [PMID: 36354494 PMCID: PMC9688674 DOI: 10.3390/bios12110985] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/26/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.
Collapse
Affiliation(s)
- Gaetano Chemello
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
| | | | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
| |
Collapse
|
20
|
Lau CH, Yu KHO, Yip TF, Luk LY, Wai AKC, Sit TY, Wong JYH, Ho JWK. An artificial intelligence-enabled smartphone app for real-time pressure injury assessment. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:905074. [PMID: 36212608 PMCID: PMC9541137 DOI: 10.3389/fmedt.2022.905074] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022] Open
Abstract
The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%–100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.
Collapse
Affiliation(s)
- Chun Hon Lau
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ken Hung-On Yu
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Tsz Fung Yip
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Luke Yik Fung Luk
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Abraham Ka Chung Wai
- Department of Emergency Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Tin-Yan Sit
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Janet Yuen-Ha Wong
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- School of Nursing / Health Studies, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong SAR, China
- Correspondence: Janet Yuen-Ha Wong Joshua Wing Kei Ho
| | - Joshua Wing Kei Ho
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Correspondence: Janet Yuen-Ha Wong Joshua Wing Kei Ho
| |
Collapse
|
21
|
Cassidy B, Reeves ND, Pappachan JM, Ahmad N, Haycocks S, Gillespie D, Yap MH. A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers. IEEE PERVASIVE COMPUTING 2022. [DOI: 10.1109/mprv.2021.3135686] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
22
|
Mahmood F, Bendayan S, Ghazawi FM, Litvinov IV. Editorial: The Emerging Role of Artificial Intelligence in Dermatology. Front Med (Lausanne) 2021; 8:751649. [PMID: 34869445 PMCID: PMC8635630 DOI: 10.3389/fmed.2021.751649] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 10/27/2021] [Indexed: 12/17/2022] Open
Affiliation(s)
- Farhan Mahmood
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | | | - Feras M Ghazawi
- Division of Dermatology, University of Ottawa, Ottawa, ON, Canada
| | - Ivan V Litvinov
- Division of Dermatology, McGill University, Montréal, QC, Canada
| |
Collapse
|
23
|
Chan KS, Liang S, Cho YT, Chan YM, Tan AHM, Muthuveerappa S, Lai TP, Goh CC, Joseph A, Hong Q, Yong E, Zhang L, Chong LRC, Tan GWL, Chandrasekar S, Lo ZJ. Clinical validation of a machine-learning-based handheld 3-dimensional infrared wound imaging device in venous leg ulcers. Int Wound J 2021; 19:436-446. [PMID: 34121320 PMCID: PMC8762571 DOI: 10.1111/iwj.13644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 06/04/2021] [Accepted: 06/05/2021] [Indexed: 12/17/2022] Open
Abstract
Chronic venous insufficiency is a chronic disease of the venous system with a prevalence of 25% to 40% in females and 10% to 20% in males. Venous leg ulcers (VLUs) result from venous insufficiency. VLUs have a prevalence of 0.18% to 1% with a 1‐year recurrence of 25% to 50%, bearing significant socioeconomic burden. It is therefore important for regular assessment and monitoring of VLUs to prevent worsening. Our study aims to assess the intra‐ and inter‐rater reliability of a machine learning‐based handheld 3‐dimensional infrared wound imaging device (WoundAide [WA] imaging system, Konica Minolta Inc, Tokyo, Japan) compared with traditional measurements by trained wound nurse. This is a prospective cross‐sectional study on 52 patients with VLUs from September 2019 to January 2021 using three WA imaging systems. Baseline patient profile and clinical demographics were collected. Basic wound parameters (length, width and area) were collected for both traditional measurements and measurements taken by the WA imaging systems. Intra‐ and inter‐rater reliability was analysed using intra‐class correlation statistics. A total of 222 wound images from 52 patients were assessed. There is excellent intra‐rater reliability of the WA imaging system on three different image captures of the same wound (intra‐rater reliability ranging 0.978‐0.992). In addition, there is excellent inter‐rater reliability between the three WA imaging systems for length (0.987), width (0.990) and area (0.995). Good inter‐rater reliability for length and width (range 0.875‐0.900) and excellent inter‐rater reliability (range 0.932‐0.950) were obtained between wound nurse measurement and each of the WA imaging system. In conclusion, high intra‐ and inter‐rater reliability was obtained for the WA imaging systems. We also obtained high inter‐rater reliability of WA measurements against traditional wound measurement. The WA imaging system is a useful clinical adjunct in the monitoring of VLU wound documentation.
Collapse
Affiliation(s)
- Kai Siang Chan
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Shanying Liang
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Yuan Teng Cho
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Yam Meng Chan
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Audrey Hui Min Tan
- Wound and Stoma Care, Nursing Service, Tan Tock Seng Hospital, Singapore, Singapore
| | | | - Tina Peiting Lai
- Wound and Stoma Care, Nursing Service, Tan Tock Seng Hospital, Singapore, Singapore
| | - Cheng Cheng Goh
- Wound and Stoma Care, Nursing Service, Tan Tock Seng Hospital, Singapore, Singapore
| | - Annie Joseph
- Skin Research Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Qiantai Hong
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Enming Yong
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Li Zhang
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Lester Rhan Chaen Chong
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Glenn Wei Leong Tan
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Sadhana Chandrasekar
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore
| | - Zhiwen Joseph Lo
- Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore, Singapore.,Skin Research Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| |
Collapse
|