Ignite Creation Date:
2024-05-06 @ 8:24 PM
Last Modification Date:
2024-10-26 @ 3:26 PM
Study NCT ID:
NCT06362629
Status:
RECRUITING
Last Update Posted:
2024-04-12
First Post:
2024-04-08
Brief Title:
AI App for Management of Atopic Dermatitis
Sponsor:
West China Hospital
Organization:
West China Hospital
Study Overview
Official Title:
Construction and Evaluation of an Artificial Intelligence Assistant Decision-making System Focused on the Treat to Target Framework and Full Process Management for Atopic Dermatitis
Status:
RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Background Atopic dermatitis AD is a chronic inflammatory skin disease characterized by recurrent rashes and itching which seriously affects the quality of life of patients and brings heavy economic burden to society The Treat to Target T2T strategy was proposed to guide optimal use of systemic therapies in patients with moderate to severe AD and it is emphasized patients adherence and combined evaluation from both health providers and patients While effective treatments for AD are available non-adherence of treatment is common in clinical practice due to the patients unawareness of self-evaluation and lack of concern about the specific follow-up time points in clinics which leads to the treatment failure and repeated relapse of AD
Hypothesis An Artificial Intelligence assistant decision-making system AIADMS with implementation of the T2T framework could help control the disease progression and improve the clinical outcomes for AD
Overall objectives
the investigators aim to develop an AIADMS in the form of smartphone app to integrate T2T approach for both clinicians and patients and design clinical trials to verify the effectiveness and safety of the app Methods This project consists of three parts AI training model for diagnosis and severity grading of AD based on deep learning development of Artificial Intelligence assistant decision-making system AIADMS in the form of app and design of a randomized controlled trial to verify the effectiveness and safety of AIADMS App for improvement of the clinical outcomes in AD patients
Expected results With application of AIADMS based app the goal of T2T for patients with AD could be realized better the prognosis could be improved and more satisfaction could be achieved for both patients and clinicians
Impact This is the first AIADMS based app for AD management running through thediagnosis patients self-participation medical follow-up and evaluation of achievement of goal of T2T
Hypothesis An Artificial Intelligence assistant decision-making system AIADMS with implementation of the T2T framework could help control the disease progression and improve the clinical outcomes for AD
Overall objectives
the investigators aim to develop an AIADMS in the form of smartphone app to integrate T2T approach for both clinicians and patients and design clinical trials to verify the effectiveness and safety of the app Methods This project consists of three parts AI training model for diagnosis and severity grading of AD based on deep learning development of Artificial Intelligence assistant decision-making system AIADMS in the form of app and design of a randomized controlled trial to verify the effectiveness and safety of AIADMS App for improvement of the clinical outcomes in AD patients
Expected results With application of AIADMS based app the goal of T2T for patients with AD could be realized better the prognosis could be improved and more satisfaction could be achieved for both patients and clinicians
Impact This is the first AIADMS based app for AD management running through thediagnosis patients self-participation medical follow-up and evaluation of achievement of goal of T2T
Detailed Description:
The project will be executed at the Department of Dermatovenereology West China Hospital of Sichuan University The protocol will be approved by the Biological- Medical Ethical Committee of the West China Hospital of Sichuan University and written informed consent will be obtained from all participants before the investigators take images of the skin lesions and before we recruit them in clinical trials
1 Automatic detection and evaluation of AD based on AI deep learning 11 Dataset of atopic dermatitis The dataset will be established from more than 10000 clinical images of AD patients for AI deep learning Low-quality images will be excluded and the images contained the surrounding background will be cropped to include only the AD lesions
12 Labelling the clinical signs of skin lesions The labelling will be completed by three certified dermatologists and three trained algorithm engineers The dermatologists will label the clinical signs including erythema papulation edema oozing excoriation lichenification and dryness and severity of each sign will be evaluated and labelled on a four-point scale 0 none 1 mild 2 moderate and 3 severe The result of each clinical sign in an image will be labelled as an example of erythema-2 edema- 2 or oozing-3 After labelling the images the dermatologists and algorithm engineers verify the quality of the labelled images from both clinical and labelling rules and cross-validate the accuracy of signs and severity Images that meet the requirements will be used for model training During the labelling and model training process the relevant personnel will be unaware of all the private patient information
13 Model training The model training will be carried out after labelling of the images
An accurate and efficient semantic segmentation model will be trained to distinguish abnormal skin lesion areas to identify all the clinical signs A fast and accurate pixel level skin segmentation model will be trained to determine the ratio of the lesion area to the overall skin area Besides an efficient and practical method to convert the segmented skin lesion area into real skin area units will be created to achieve the accurate restoration of the true size as much as possible from the distortion of the skin lesion because of the shooting distance angle or automatic enhancement The dataset will be divided for training validation and testing Images of 6500 of 10000 will be used in training and validation of the proposed model and images of the remaining 3500 of 10000 will be used for testing After training combined with the different questionnaire items filled by patients the evaluative tools including EASI SCORED POEM pp-NRS and DLQI will be calculated by the model
2 Development of the AIADMS app The app will support the Android system and IOS system and it will be designed as two versions for both patients and clinicians with the distinguished login entrance The fundamental function of the app will include Push Reminder Upload Evaluation and Data management
21 The Push function is designed to transmit information to patients and medical staff The pushed information could be received and displayed on the screen of the mobile phone even if the app is not opened and the mobile phone is in the locked screen state and the users can set the time of receiving the pushed information by themselves For example the predetermined time point for follow-up in clinics will be presented as You should come to see the doctor on next Monday July 25 2023 The Push function can activate the use of app increase the viscosity of users and drive the utilization of other functional modules
22 The Reminder function is mainly used for reminding the patients of taking medicine uploading photos of skin lesions self-evaluation and scheduled follow-up
23 The Upload function is designed to help patients participate in the systemic treatment They can upload their photos of skin lesions the description of progresses of AD or questionnaires
24 The Evaluation function is developed to provide information for both patients and medical staff By uploading photos of skin lesions and filling in the different questionnaire items the app will automatically evaluate the severity of lesions and calculate the EASI POEM PP-NRS SCORAD or DLQI scores This function could help patients know more about their situation of the disease and take part in self- evaluation and self-care as the T2T strategy recommended
24 The Data management function is designed for medical staff to manage the patients more conveniently and design the medical research They can log in to the app platform website to collect and export data carry out statistical analysis and big data mining App itself can also make simple statistics and management of data For example data such as EASI POEM and PP-NRS score at the time points of before treatment 2 weeks 4 weeks 12 weeks and 6 months after treatment could be automatically generated into statistical reports to presented in the form of histograms or curves App can also be further improved and updated to the new version through the analysis of users habits and the function modules could be optimized with the high frequency of use and the feedback from both medical staff and patients
3 Effectiveness and safety of AIADMS App for improvement of the clinical outcomes in AD patients a randomized controlled trial
This trial is a single centered prospective randomized controlled trial that test the superiority of the implementation of T2T strategy by application of AIADMS app in patients with AD in term of improvement of clinical outcomes
This would the first AI assisted tool for AD during the process of diagnosis management and follow-up It will provide solid evidence for the application of AI in dermatology worldwidely
1 Automatic detection and evaluation of AD based on AI deep learning 11 Dataset of atopic dermatitis The dataset will be established from more than 10000 clinical images of AD patients for AI deep learning Low-quality images will be excluded and the images contained the surrounding background will be cropped to include only the AD lesions
12 Labelling the clinical signs of skin lesions The labelling will be completed by three certified dermatologists and three trained algorithm engineers The dermatologists will label the clinical signs including erythema papulation edema oozing excoriation lichenification and dryness and severity of each sign will be evaluated and labelled on a four-point scale 0 none 1 mild 2 moderate and 3 severe The result of each clinical sign in an image will be labelled as an example of erythema-2 edema- 2 or oozing-3 After labelling the images the dermatologists and algorithm engineers verify the quality of the labelled images from both clinical and labelling rules and cross-validate the accuracy of signs and severity Images that meet the requirements will be used for model training During the labelling and model training process the relevant personnel will be unaware of all the private patient information
13 Model training The model training will be carried out after labelling of the images
An accurate and efficient semantic segmentation model will be trained to distinguish abnormal skin lesion areas to identify all the clinical signs A fast and accurate pixel level skin segmentation model will be trained to determine the ratio of the lesion area to the overall skin area Besides an efficient and practical method to convert the segmented skin lesion area into real skin area units will be created to achieve the accurate restoration of the true size as much as possible from the distortion of the skin lesion because of the shooting distance angle or automatic enhancement The dataset will be divided for training validation and testing Images of 6500 of 10000 will be used in training and validation of the proposed model and images of the remaining 3500 of 10000 will be used for testing After training combined with the different questionnaire items filled by patients the evaluative tools including EASI SCORED POEM pp-NRS and DLQI will be calculated by the model
2 Development of the AIADMS app The app will support the Android system and IOS system and it will be designed as two versions for both patients and clinicians with the distinguished login entrance The fundamental function of the app will include Push Reminder Upload Evaluation and Data management
21 The Push function is designed to transmit information to patients and medical staff The pushed information could be received and displayed on the screen of the mobile phone even if the app is not opened and the mobile phone is in the locked screen state and the users can set the time of receiving the pushed information by themselves For example the predetermined time point for follow-up in clinics will be presented as You should come to see the doctor on next Monday July 25 2023 The Push function can activate the use of app increase the viscosity of users and drive the utilization of other functional modules
22 The Reminder function is mainly used for reminding the patients of taking medicine uploading photos of skin lesions self-evaluation and scheduled follow-up
23 The Upload function is designed to help patients participate in the systemic treatment They can upload their photos of skin lesions the description of progresses of AD or questionnaires
24 The Evaluation function is developed to provide information for both patients and medical staff By uploading photos of skin lesions and filling in the different questionnaire items the app will automatically evaluate the severity of lesions and calculate the EASI POEM PP-NRS SCORAD or DLQI scores This function could help patients know more about their situation of the disease and take part in self- evaluation and self-care as the T2T strategy recommended
24 The Data management function is designed for medical staff to manage the patients more conveniently and design the medical research They can log in to the app platform website to collect and export data carry out statistical analysis and big data mining App itself can also make simple statistics and management of data For example data such as EASI POEM and PP-NRS score at the time points of before treatment 2 weeks 4 weeks 12 weeks and 6 months after treatment could be automatically generated into statistical reports to presented in the form of histograms or curves App can also be further improved and updated to the new version through the analysis of users habits and the function modules could be optimized with the high frequency of use and the feedback from both medical staff and patients
3 Effectiveness and safety of AIADMS App for improvement of the clinical outcomes in AD patients a randomized controlled trial
This trial is a single centered prospective randomized controlled trial that test the superiority of the implementation of T2T strategy by application of AIADMS app in patients with AD in term of improvement of clinical outcomes
This would the first AI assisted tool for AD during the process of diagnosis management and follow-up It will provide solid evidence for the application of AI in dermatology worldwidely
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None