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NCT Number: NCT04679961

Deep Learning on 3D Cellular-resolution Tomogram

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.

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Key information

Conditions

Skin Diseases Skin and Connective Tissue Diseases

Age range

20 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Mackay Memorial Hospital

New Taipei City, Tamsui District, 25160, Taiwan

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

ApolloVue® S100 Image System (Apollo Medical Optics)

Device

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: 510(K) Number: K201552 (class II)

Primary outcomes

  1. 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.

  2. 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.

Secondary outcomes

  1. 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.

Sponsors and collaborators

Lead sponsor

Mackay Memorial Hospital

Other

Collaborators

  • National Taiwan University

Registry information

Important dates

Study start
2020
Primary completion
2022
Study completion
2022
First posted
Dec 22, 2020
Registry last updated
Mar 17, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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