- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04679961
Deep Learning on 3D Cellular-resolution Tomogram
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
Introduction Optical coherent tomography (OCT) technology has been widely used in medical practice, such as ophthalmology. The application in dermatology is slowly progressed until the marked improvement of resolution recently. One of the newly designed OCT devices using in this study is based on the research and development of Professor Sheng-Lung Huang of National Taiwan University. The light source was made with original glass-covered crystalline fiber which has successfully provided sub-micron resolution on the skin, which is better than the traditional 5-10 micron resolution of high-definition OCT. This new OCT system (ApolloVue™ S100 image system, Viper1-S003, Apollo Medical Optics) has been used in this previous clinical trial "in vivo OCT images of different skin diseases" without adverse events. OCT images of different skin diseases collected in that trial were compared with HE-stained pathological sections. They provided useful information to physicians. The risk-benefit assessment of this clinical trial is the same as expected. The risk is low in clinical use, and both for the operators and the subjects. In recent years, the application of artificial intelligence technology in the analysis of tissue classification of medical images is rapidly developing. Therefore, we are going to use deep learning technology to improve the interpretation of OCT images to help the subsequent diagnosis of skin diseases.
Inclusion criteria
Experimental group:
- Adults aged 20 years or older
- Non-treat lesion of epidermal inflammatory disease: dermatitis and psoriasis: 300 participants.
- Benign tumors: seborrheic keratosis and nevus: 300 participants
- Malignant tumors: actinic keratosis (AK), melanoma, basal cell carcinoma (BCC), Bowen's disease, squamous cell carcinoma (SCC), and extramammary Paget's disease (EMPD): 100 participants
- Pigmented diseases: solar lentigo, melasma, and vitiligo: 300 participants
Control group:
The healthy face (exposed site) and inner forearm (unexposed site) skin of epidermal tumors and pigmented diseases of the above experimental group were used as a control group, excluding epidermal inflammatory diseases, 700 participants in the control group were expected.
Exclusion criteria
Experimental group:
- Minors aged under 20 years
- Suspected a transcutaneous infectious disease, including infections such as bacteria, fungi, viruses, and parasites.
- All skin tumors that are in the subcutaneous tissue
- All skin lesions are open wounds
- All skin lesions are in a location that is difficult to scan
- Not willing to cooperate with methods and related procedures of this study
- Vulnerable populations, including prisoners, pregnant women, handicapped, mentally disabled, known AIDS patients, and homelessness
Control group:
- Minors under 20 years of age.
- Epidermal inflammatory disease
- Suspected a transcutaneous infectious disease, including infections such as bacteria, fungi, viruses, and parasites.
- Individuals who have a systemic skin disorder.
- Individuals who have a history of severe skin condition
- Individuals with surgeries/cosmetic surgeries/micro cosmetic surgery (eg. cosmetic injections and/or laser etc.) on healthy skin at face and inner forearm in last 3 months and a physician determine the surgery will affect outcome of the OCT images.
- Not willing to cooperate with methods and related procedures of this study
- Vulnerable populations, including prisoners, pregnant women, handicapped, mentally disabled, known AIDS patients, and homelessness
Deep convolutional neural network (DCNN) was used to mark tissue and lesions in OCT images. When training DCNN models, transfer learning strategies will be used to fine-tune the parameters from pre-trained models that contain a lot of image knowledge, such as GoogLeNet, rather than training the models from scratch. This method retains the low-level image knowledge common to natural and medical images, and significantly reduces the time to train the model. During the training process, the parameters of the first few layers that store the low-order image knowledge in the model are fixed, and the parameters of the subsequent layers of the model are changed by the back-propagation algorithm. Finally, a layer of linear classifier is added to the end of the DCNN to determine the type / size of the symptoms in the input image.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Contact
- Name: Yu-Hung Chen, PI
- Phone Number: 2556 +886-2543-3535
- Email: dr.yhwu@gmail.com
Study Locations
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Tamsui District
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New Taipei City, Tamsui District, Taiwan, 25160
- Mackay Memorial Hospital
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion criteria
Experimental group:
- Adults aged 20 years or older
- Non-treat lesion of epidermal inflammatory disease: dermatitis and psoriasis
- Benign tumors: seborrheic keratosis and nevus
- Malignant tumors: actinic keratosis (AK), melanoma, basal cell carcinoma (BCC), Bowen's disease, squamous cell carcinoma (SCC), and extramammary Paget's disease (EMPD)
- Pigmented diseases: solar lentigo, melasma, and vitiligo
Control group:
The healthy face (exposed site) and inner forearm (unexposed site) skin of epidermal tumors and pigmented diseases of the above experimental group were used as a control group, excluding epidermal inflammatory diseases.
Exclusion criteria
Experimental group:
- Minors aged under 20 years
- Suspected a transcutaneous infectious disease, including infections such as bacteria, fungi, viruses, and parasites.
- All skin tumors that are in the subcutaneous tissue
- All skin lesions are open wounds
- All skin lesions are in a location that is difficult to scan
- Not willing to cooperate with methods and related procedures of this study
- Vulnerable populations, including prisoners, pregnant women, handicapped, mentally disabled, known AIDS patients, and homelessness
Control group:
- Minors under 20 years of age.
- Epidermal inflammatory disease
- Suspected a transcutaneous infectious disease, including infections such as bacteria, fungi, viruses, and parasites.
- Individuals who have a systemic skin disorder.
- Individuals who have a history of severe skin condition
- Individuals with surgeries/cosmetic surgeries/micro cosmetic surgery (eg. cosmetic injections and/or laser etc.) on healthy skin at face and inner forearm in last 3 months and a physician determine the surgery will affect outcome of the OCT images.
- Not willing to cooperate with methods and related procedures of this study
- Vulnerable populations, including prisoners, pregnant women, handicapped, mentally disabled, known AIDS patients, and homelessness
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Experimental
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The device is an in vivo non-invasive optical coherence tomography and will be used to obtain at least 6 medical images of normal and lesional skin, respectively, for both experimental group and control group.
Other Names:
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Control
Healthy skin
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The device is an in vivo non-invasive optical coherence tomography and will be used to obtain at least 6 medical images of normal and lesional skin, respectively, for both experimental group and control group.
Other Names:
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Number of subjects of tomograms that can be analyzed by artificial intelligence techniques
Time Frame: 2.5 years
|
Number of subjects of tomograms that can be analyzed by artificial intelligence techniques (including machine learning and deep learning) will be compared to that cannot be analyzed to identify the feasibility of using artificial intelligence techniques to analyze tomograms at study completion.
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2.5 years
|
Number of subjects with the similarity results of interpreting tomograms between artificial intelligence and experts
Time Frame: 2.5 years
|
Number of subjects with the similarity results of interpreting tomograms between artificial intelligence and experts will be compared to that with no similarity to verify whether artificial intelligence interpretation are comparable with gold standard methods expert interpretation at study completion.
|
2.5 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Number of subjects with the correlation between tomograms and gold standard methods, eg. existing clinical images or pathological images.
Time Frame: 2.5 years
|
Number of subjects with the correlation between tomograms and gold standard methods, eg.
existing clinical images (including photographs, dermoscopic images, etc.) or pathological images (including H&E stain, etc.) will be compared to that with no correlation to verify whether the tomograms are comparable with above gold standard methods at study completion.
|
2.5 years
|
Collaborators and Investigators
Sponsor
Collaborators
Publications and helpful links
General Publications
- Chang CK, Tsai CC, Hsu WY, Chen JS, Liao YH, Sheen YS, Hong JB, Lin MY, Tjiu JW, Huang SL. Errata: Segmentation of nucleus and cytoplasm of a single cell in three-dimensional tomogram using optical coherence tomography. J Biomed Opt. 2017 Mar 1;22(3):39801. doi: 10.1117/1.JBO.22.3.039801. No abstract available.
- Tsai CC, Chang CK, Hsu KY, Ho TS, Lin MY, Tjiu JW, Huang SL. Full-depth epidermis tomography using a Mirau-based full-field optical coherence tomography. Biomed Opt Express. 2014 Aug 8;5(9):3001-10. doi: 10.1364/BOE.5.003001. eCollection 2014 Sep 1.
- Wang YJ, Huang YK, Wang JY, Wu YH. In vivo characterization of large cell acanthoma by cellular resolution optical coherent tomography. Photodiagnosis Photodyn Ther. 2019 Jun;26:199-202. doi: 10.1016/j.pdpdt.2019.03.020. Epub 2019 Mar 30. No abstract available.
- Schneider SL, Kohli I, Hamzavi IH, Council ML, Rossi AM, Ozog DM. Emerging imaging technologies in dermatology: Part I: Basic principles. J Am Acad Dermatol. 2019 Apr;80(4):1114-1120. doi: 10.1016/j.jaad.2018.11.042. Epub 2018 Dec 4.
- Schneider SL, Kohli I, Hamzavi IH, Council ML, Rossi AM, Ozog DM. Emerging imaging technologies in dermatology: Part II: Applications and limitations. J Am Acad Dermatol. 2019 Apr;80(4):1121-1131. doi: 10.1016/j.jaad.2018.11.043. Epub 2018 Dec 4.
- Dubois A, Levecq O, Azimani H, Siret D, Barut A, Suppa M, Del Marmol V, Malvehy J, Cinotti E, Rubegni P, Perrot JL. Line-field confocal optical coherence tomography for high-resolution noninvasive imaging of skin tumors. J Biomed Opt. 2018 Oct;23(10):1-9. doi: 10.1117/1.JBO.23.10.106007.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- 20STW2-01
- MOST 108-2634-F-002-014 - (Other Grant/Funding Number: Ministry of Science and Technology, Taiwan)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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