Skin DiseasesNCT04679961
Deep Learning on 3D Cellular-resolution Tomogram
An observational study of Skin Diseases, run by Mackay Memorial Hospital at 1 site in Taiwan.
Trial summary
Skin biopsy is the main method to diagnose skin tumors, skin inflammation, and pigmented diseases. However, biopsy is an invasive method that can cause wounds and scars. Optical coherent tomography (OCT) technology is a fast, non-invasive, non-radioactive, and label-free imaging method. Read more about this study
- Who is it for?
- Ages 20 years and olderHealthy volunteers can take part
- Conditions the study is for
- Skin Diseases
- What is being tested?
- ApolloVue® S100 Image System (Apollo Medical Optics)
- Type of treatment
- Medical device
- How many people
- 107 participants
- Where
- 1 siteTaiwan
- Study dates
- 21 Dec 2020to 14 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The population from both experimental group and control group will be selected.
Who may be able to join
- 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.
Who cannot join
- 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
Show all 17Show fewer
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Skin biopsy is the main method to diagnose skin tumors, skin inflammation, and pigmented diseases. However, biopsy is an invasive method that can cause wounds and scars. Optical coherent tomography (OCT) technology is a fast, non-invasive, non-radioactive, and label-free imaging method. This technology generates real-time images of living tissue by detecting the variations in the refractive indexes of various components in soft tissues. Recently, there is a breakthrough progress that the newly designed ultrahigh resolution OCT can provide in vivo cellular resolution similar to histopathological sections in the high magnification. In our previous clinical trial "Early feasibility study: application of OCT imaging in dermatology" (approved by IRB of MacKay Memorial Hospital, no. 17CT062Be), it showed characteristic features of different skin inflammatory diseases and tumors can be distinguished successfully in tomograms. There were no adverse event or serious adverse event in this trial. Artificial intelligence technologies have been used widely in the image analysis in recent years. Hence, we aim to collect OCT tomograms of common skin inflammatory diseases, skin tumors, and pigmented diseases, and compare with normal skin for machine learning. We expect the integration of tomograms with deep learning artificial intelligence may assist identifying histological features in these images and provide new alternative way for non-invasive diagnosis in dermatology.
Read the 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.
- Conditions
- Skin Diseases
- Being tested
- ApolloVue® S100 Image System (Apollo Medical Optics)
- Keywords
- Optical coherence tomography (OCT), artificial intelligence, deep learning, skin diseases, dermatology
- Medical subject headings
- Skin Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ApolloVue® S100 Image System (Apollo Medical Optics)Device
Also called 510(K) Number: K201552 (class II)
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.
| Study group | Role | What they receive |
|---|---|---|
| Experimental | 1. Epidermal inflammations, including eczematous diseases and psoriasis
2. Epidermal tumors, including benign tumors and malignant tumors
3. Pigmented diseases, including hypopigmentation and hyperpigmentation Receives: Device: ApolloVue® S100 Image System (Apollo Medical Optics) | |
| Control | Healthy skin Receives: Device: ApolloVue® S100 Image System (Apollo Medical Optics) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of subjects of tomograms that can be analyzed by artificial intelligence techniques
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.
Measured: 2.5 years
Number of subjects with the similarity results of interpreting tomograms between artificial intelligence and experts
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.
Measured: 2.5 years
Secondary outcomes (1)
Number of subjects with the correlation between tomograms and gold standard methods, eg. existing clinical images or pathological images.
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.
Measured: 2.5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Dec 2020
- Study start21 Dec 2020
- Primary completion14 Dec 2022
- Study completion14 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Skin DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 107 participants (actual)
Sponsor and investigators
- Lead sponsor
- Mackay Memorial Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Taiwan University
- Responsible party
- Principal investigator, Yu-Hung Wu, Mackay Memorial Hospital
- Investigators
- Wu, MD, Principal investigator, Mackay Memorial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04679961
- Sponsor's protocol ID
- 20STW2-01
- Other IDs
- MOST 108-2634-F-002-014 -
- Status last verified
- Mar 2023
- First submitted
- 18 Dec 2020
- Last updated
- 17 Mar 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 30528311Schneider 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.
PubMed 30528310Dubois 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.
PubMed 30353716Wang 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.
PubMed 30940575Tsai 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.
PubMed 25401013Chang 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.
PubMed 28300273
Study at a glance
From the registry record, last updated by the sponsor on 17 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning on 3D Cellular-resolution Tomogram
- ClinicalTrials.gov ID
- NCT04679961
- Acronym / sponsor ID
- 20STW2-01
- Conditions
- Skin Diseases
- Keywords
- Optical coherence tomography (OCT), artificial intelligence, deep learning, skin diseases, dermatology
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 107 participants (actual)
- Lead sponsor
- Mackay Memorial HospitalUniversity, hospital or other
- Collaborators
- National Taiwan University
- Study start
- 21 Dec 2020 (actual)
- Primary completion
- 14 Dec 2022 (actual)
- Study completion
- 14 Dec 2022 (actual)
- First posted
- 22 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Skin biopsy is the main method to diagnose skin tumors, skin inflammation, and pigmented diseases. However, biopsy is an invasive method that can cause wounds and scars. Optical coherent tomography (OCT) technology is a fast, non-invasive, non-radioactive, and label-free imaging method. This technology generates real-time images of living tissue by detecting the variations in the refractive indexes of various components in soft tissues. Recently, there is a breakthrough progress that the newly designed ultrahigh resolution OCT can provide in vivo cellular resolution similar to histopathological sections in the high magnification. In our previous clinical trial "Early feasibility study: application of OCT imaging in dermatology" (approved by IRB of MacKay Memorial Hospital, no. 17CT062Be), it showed characteristic features of different skin inflammatory diseases and tumors can be distinguished successfully in tomograms. There were no adverse event or serious adverse event in this trial. Artificial intelligence technologies have been used widely in the image analysis in recent years. Hence, we aim to collect OCT tomograms of common skin inflammatory diseases, skin tumors, and pigmented diseases, and compare with normal skin for machine learning. We expect the integration of tomograms with deep learning artificial intelligence may assist identifying histological features in these images and provide new alternative way for non-invasive diagnosis in dermatology.
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 design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Experimental | – | Device: ApolloVue® S100 Image System (Apollo Medical Optics)1. Epidermal inflammations, including eczematous diseases and psoriasis 2. Epidermal tumors, including benign tumors and malignant tumors 3. Pigmented diseases, including hypopigmentation and hyperpigmentation |
| Control | – | Device: ApolloVue® S100 Image System (Apollo Medical Optics)Healthy skin |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The population from both experimental group and control group will be selected.
Key inclusion criteria (8 listed)
- 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
Key exclusion criteria (17 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ApolloVue® S100 Image System (Apollo Medical Optics)Also: 510(K) Number: K201552 (class II)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. | Device | Control, Experimental |
Primary outcome
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.
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.
1 secondary outcome
Number of subjects with the correlation between tomograms and gold standard methods, eg. existing clinical images or pathological images.
Time frame: 2.5 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Mar 2023.
- Background: PubMed 30528311Schneider 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.
- Background: PubMed 30528310Schneider 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.
- Background: PubMed 30353716Dubois 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.
- Background: PubMed 30940575Wang 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.
- Background: PubMed 25401013Tsai 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.
- Background: PubMed 28300273Chang 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.
- Individual participant dataSharing planned: Undecided
Related trials for Skin Diseases
RecruitingObservational study
Treatment Effectiveness in People With axSpA or PsA Starting Treatment With Bimekizumab, Risankizumab, Guselkumab, Upadacitinib, or a TNF Inhibitor
Axial Spondylarthritis (axSpA), Psoriatic Arthritis (PsA)
Enrolling by invitationObservational study
Allergy, Asthma, and Atopic Eczema in Finland
Asthma, Asthma in Children
Enrolling by invitationObservational study
Establishing Fibroblast-Derived Cell Lines From Skin Biopsies of Patients With Immunodeficiency or Immunodysregulation Disorders
Primary Immunodeficiency, DOCK8
Enrolling by invitation
Study to Evaluate Xtressé Gummy Supplement in Men With Androgenic Alopecia
Androgenic Alopecia, Hair Loss/Baldness
Not yet recruiting
Decision Support for Chemoprevention
Breast Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Wu, MD principal investigator
Mackay Memorial Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Skin Diseases, run by Mackay Memorial Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 17 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Mar 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of subjects of tomograms that can be analyzed by artificial intelligence techniques (time frame: 2.5 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04679961, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Mar 2023.