xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT06029751</url>
    </required_header>
    <id_info>
      <org_study_id>DHZhejiangU-2022(005)</org_study_id>
      <nct_id>NCT06029751</nct_id>
    </id_info>
    <brief_title>Dynamic Follow-up of Factors Influencing Implant Success and Models for Predicting Implant Outcomes</brief_title>
    <official_title>Dynamic Follow-up of Factors Influencing Implant Success and Models for Predicting Implant Outcomes</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>The Dental Hospital of Zhejiang University School of Medicine</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>The Dental Hospital of Zhejiang University School of Medicine</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        Nowadays, artificial intelligence technology with machine learning as the main means has been&#xD;
        increasingly applied to the oral field, and has played an increasingly important role in the&#xD;
        examination, diagnosis, treatment and prognosis assessment of oral diseases. Among them,&#xD;
        machine learning is an important branch of artificial intelligence, which refers to the&#xD;
        system learning specific statistical patterns in a given data set to predict the behavior of&#xD;
        new data samples [8]. Machine learning is divided into two main categories: Supervised&#xD;
        learning and Unsupervised learning. Whether there is supervision depends on whether the data&#xD;
        entered is labeled or not. If the input data is labeled, it is supervised learning. Unlabeled&#xD;
        learning is unsupervised. Supervised learning is a kind of learning algorithm when the&#xD;
        correct output of the data set is known. Because the input and output are known, it means&#xD;
        that there is a relationship between the input and output, and the supervised learning&#xD;
        algorithm is to discover and summarize this &quot;relationship&quot;. Unsupervised learning refers to a&#xD;
        class of learning algorithms for unlabeled data. The absence of label information means that&#xD;
        patterns or structures need to be discovered and summarized from the data set.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Starting from different data types, researchers built a variety of models to mine the data&#xD;
        itself and predict the prognosis of the implant. Machine learning is often more impressive&#xD;
        and intuitive in terms of images. In the field of oral implantology, researchers analyze&#xD;
        preoperative image data based on machine learning to identify important anatomical structures&#xD;
        (such as maxillary sinus, mandibular neural tube, etc.) and analyze alveolar bone quality.&#xD;
        Large-scale imaging data is also used to identify the different implant systems on the&#xD;
        market. Machine learning also plays an important role in the development of implant surgery&#xD;
        plans, which is conducive to more accurate and efficient implantation surgery. The evaluation&#xD;
        of implant retention rate and individual bone level is also one of the key clinical concerns.&#xD;
        Most methods to study such issues are: Kaplan-Meier survival analysis, Cox survival analysis,&#xD;
        etc., to study implant retention rate and influencing factors. Linear (mixed) model and&#xD;
        multiple logistic regression were used to study the changes and influencing factors of bone&#xD;
        absorption at implant edge. However, in daily clinical practice, there may be some practical&#xD;
        problems such as lost follow-up and partial data missing. As the clinical scenarios of&#xD;
        research become more and more clear, even partial data missing often leads to results that&#xD;
        cannot be accurately evaluated and predicted. Therefore, in terms of supervised learning,&#xD;
        this study aims to establish a predictive model of implant bone level change and evaluate the&#xD;
        accuracy of the model through machine learning of implant edge bone level (MBL) with large&#xD;
        amounts of data. In terms of unsupervised learning, the aim is to identify susceptibility&#xD;
        phenotypes to implant failure through: clustering of individual-related information about&#xD;
        implants.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">January 1, 2017</start_date>
    <completion_date type="Anticipated">December 31, 2025</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2025</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Other</observational_model>
      <time_perspective>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Mean Bone Level of dental implant</measure>
      <time_frame>1-7 years</time_frame>
      <description>The vertical distance between the implant and the first contact area of bone and the tip of the implant (mesial and distal)</description>
    </primary_outcome>
    <enrollment type="Anticipated">1000</enrollment>
    <condition>Implant Site Reaction</condition>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>No intervention</intervention_name>
      <description>No intervention</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          This was a retrospective study. After searching the literature identified by the sample&#xD;
          size of the combined model prediction method, it was determined that the initial data and&#xD;
          follow-up data of 1000 patients were required.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  (1) Patients aged 18 years and above; (2) 1-5 years after implantation; (3)&#xD;
               Implantation torque &gt; 35N·cm; (4) Signed informed consent.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  (1) Contraindications of general implantation surgery; (2) Have received head and neck&#xD;
               radiation therapy; (3) Past or current treatment with bisphosphonates; (4) Do not&#xD;
               cooperate with the interviewer.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Weida Li</last_name>
      <role>Principal Investigator</role>
      <affiliation>Stomatological Hospital Affiliated to Zhejiang University School of Medicine</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Yi Zhou</last_name>
      <phone>87217419</phone>
      <phone_ext>0571</phone_ext>
      <email>zhouyizyzyzy@163.com</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Siyao Ma</last_name>
      <phone>87217419</phone>
      <phone_ext>0571</phone_ext>
      <email>1123348672@qq.com</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>The Stomatologic Hospital, School of Medicine, Zhejiang University</name>
        <address>
          <city>Hangzhou</city>
          <state>Zhejiang</state>
          <zip>310003</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Yi Zhou</last_name>
        <phone>87217419</phone>
        <phone_ext>0571</phone_ext>
      </contact>
      <contact_backup>
        <last_name>Siyao Ma</last_name>
        <phone>87217419</phone>
        <phone_ext>0571</phone_ext>
      </contact_backup>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <reference>
      <citation>Papantonopoulos G, Gogos C, Housos E, Bountis T, Loos BG. Prediction of individual implant bone levels and the existence of implant &quot;phenotypes&quot;. Clin Oral Implants Res. 2017 Jul;28(7):823-832. doi: 10.1111/clr.12887. Epub 2016 Jun 1.</citation>
      <PMID>27252014</PMID>
    </reference>
    <reference>
      <citation>Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly E, Buchler M, Ladriere M, Le Quintrec M, Delahousse M, Juric I, Basic-Jukic N, Crespo M, Silva HT Jr, Linhares K, Ribeiro de Castro MC, Soler Pujol G, Empana JP, Ulloa C, Akalin E, Bohmig G, Huang E, Stegall MD, Bentall AJ, Montgomery RA, Jordan SC, Oberbauer R, Segev DL, Friedewald JJ, Jouven X, Legendre C, Lefaucheur C, Loupy A. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021 Dec;3(12):e795-e805. doi: 10.1016/S2589-7500(21)00209-0. Epub 2021 Oct 28.</citation>
      <PMID>34756569</PMID>
    </reference>
    <reference>
      <citation>Cetiner D, Isler SC, Bakirarar B, Uraz A. Identification of a Predictive Decision Model Using Different Data Mining Algorithms for Diagnosing Peri-implant Health and Disease: A Cross-Sectional Study. Int J Oral Maxillofac Implants. 2021 Sep-Oct;36(5):952-965. doi: 10.11607/jomi.8965.</citation>
      <PMID>34698722</PMID>
    </reference>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 1, 2023</study_first_submitted>
    <study_first_submitted_qc>September 1, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 8, 2023</study_first_posted>
    <last_update_submitted>September 1, 2023</last_update_submitted>
    <last_update_submitted_qc>September 1, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>The Dental Hospital of Zhejiang University School of Medicine</investigator_affiliation>
      <investigator_full_name>Yi Zhou</investigator_full_name>
      <investigator_title>Deputy Chief Physician, Deputy Director of Dental Implant Department</investigator_title>
    </responsible_party>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06029751
  org study id: DHZhejiangU-2022(005)
  nct id: NCT06029751
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  Nowadays, artificial intelligence technology with machine learning as the main means has been
        increasingly applied to the oral field, and has played an increasingly important role in the
        examination, diagnosis, treatment and prognosis assessment of oral diseases. Among them,
        machine learning is an important branch of artificial intelligence, which refers to the
        system learning specific statistical patterns in a given data set to predict the behavior of
        new data samples [8]. Machine learning is divided into two main categories: Supervised
        learning and Unsupervised learning. Whether there is supervision depends on whether the data
        entered is labeled or not. If the input data is labeled, it is supervised learning. Unlabeled
        learning is unsupervised. Supervised learning is a kind of learning algorithm when the
        correct output of the data set is known. Because the input and output are known, it means
        that there is a relationship between the input and output, and the supervised learning
        algorithm is to discover and summarize this "relationship". Unsupervised learning refers to a
        class of learning algorithms for unlabeled data. The absence of label information means that
        patterns or structures need to be discovered and summarized from the data set.
  Starting from different data types, researchers built a variety of models to mine the data
        itself and predict the prognosis of the implant. Machine learning is often more impressive
        and intuitive in terms of images. In the field of oral implantology, researchers analyze
        preoperative image data based on machine learning to identify important anatomical structures
        (such as maxillary sinus, mandibular neural tube, etc.) and analyze alveolar bone quality.
        Large-scale imaging data is also used to identify the different implant systems on the
        market. Machine learning also plays an important role in the development of implant surgery
        plans, which is conducive to more accurate and efficient implantation surgery. The evaluation
        of implant retention rate and individual bone level is also one of the key clinical concerns.
        Most methods to study such issues are: Kaplan-Meier survival analysis, Cox survival analysis,
        etc., to study implant retention rate and influencing factors. Linear (mixed) model and
        multiple logistic regression were used to study the changes and influencing factors of bone
        absorption at implant edge. However, in daily clinical practice, there may be some practical
        problems such as lost follow-up and partial data missing. As the clinical scenarios of
        research become more and more clear, even partial data missing often leads to results that
        cannot be accurately evaluated and predicted. Therefore, in terms of supervised learning,
        this study aims to establish a predictive model of implant bone level change and evaluate the
        accuracy of the model through machine learning of implant edge bone level (MBL) with large
        amounts of data. In terms of unsupervised learning, the aim is to identify susceptibility
        phenotypes to implant failure through: clustering of individual-related information about
        implants.
  observational model: Other
  time perspective: Retrospective
  measure: Mean Bone Level of dental implant
  time frame: 1-7 years
  description: The vertical distance between the implant and the first contact area of bone and the tip of the implant (mesial and distal)
  intervention type: Other
  intervention name: No intervention
  description: No intervention
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Weida Li
  role: Principal Investigator
  affiliation: Stomatological Hospital Affiliated to Zhejiang University School of Medicine
  last name: Yi Zhou
  phone: 87217419
  phone ext: 0571
  email: zhouyizyzyzy@163.com
  last name: Siyao Ma
  phone: 87217419
  phone ext: 0571
  email: 1123348672@qq.com
  facility: 
  status: Recruiting
  contact: 
  contact backup: 
  country: China
  citation: Papantonopoulos G, Gogos C, Housos E, Bountis T, Loos BG. Prediction of individual implant bone levels and the existence of implant "phenotypes". Clin Oral Implants Res. 2017 Jul;28(7):823-832. doi: 10.1111/clr.12887. Epub 2016 Jun 1.
  PMID: 27252014
  citation: Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly E, Buchler M, Ladriere M, Le Quintrec M, Delahousse M, Juric I, Basic-Jukic N, Crespo M, Silva HT Jr, Linhares K, Ribeiro de Castro MC, Soler Pujol G, Empana JP, Ulloa C, Akalin E, Bohmig G, Huang E, Stegall MD, Bentall AJ, Montgomery RA, Jordan SC, Oberbauer R, Segev DL, Friedewald JJ, Jouven X, Legendre C, Lefaucheur C, Loupy A. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021 Dec;3(12):e795-e805. doi: 10.1016/S2589-7500(21)00209-0. Epub 2021 Oct 28.
  PMID: 34756569
  citation: Cetiner D, Isler SC, Bakirarar B, Uraz A. Identification of a Predictive Decision Model Using Different Data Mining Algorithms for Diagnosing Peri-implant Health and Disease: A Cross-Sectional Study. Int J Oral Maxillofac Implants. 2021 Sep-Oct;36(5):952-965. doi: 10.11607/jomi.8965.
  PMID: 34698722
  responsible party type: Principal Investigator
  investigator affiliation: The Dental Hospital of Zhejiang University School of Medicine
  investigator full name: Yi Zhou
  investigator title: Deputy Chief Physician, Deputy Director of Dental Implant Department
  sharing ipd: No