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Die klinische Studie NCT06791473 für Tumor ist offene rekrutierung. In der Kartenansicht des Klinische Studien Radar und den KI-Entdeckungstools finden Sie alle Details. Oder stellen Sie hier Ihre Fragen. | ||
Eine Studie entspricht den Filterkriterien
Kartenansicht
AI-Driven Cancer Diagnosis and Prediction With EHR 1.000.000 Multizentrisch
Die Details der klinischen Studie sind hauptsächlich auf Englisch verfügbar. Trial Radar KI kann jedoch helfen! Klicken Sie einfach auf 'Studie erklären', um die Informationen zur Studie in der ausgewählten Sprache anzuzeigen und zu besprechen.
Die klinische Studie NCT06791473 ist eine beobachtungsstudie zur Untersuchung von Tumor und hat den Status offene rekrutierung. Die Studie startete am 19. Januar 2025 und soll 1.000.000 Teilnehmer aufnehmen. Durchgeführt von The Eye Hospital of Wenzhou Medical University ist der Abschluss für 1. Oktober 2025 geplant. Die Daten von ClinicalTrials.gov wurden zuletzt am 30. Juli 2025 aktualisiert.
Kurzbeschreibung
This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing cancer, leveraging multimodal health data.
Ausführliche Beschreibung
Cancer diagnosis and early detection are crucial for improving patient outcomes and survival rates. Early identification of cancers and appropriate intervention can significantly impact treatment success and prognosis. In clinical practice, oncologists often need to integrate a variety of patient data-including medical history, laboratory test results, imaging data such as CT scans and MRIs, and genetic markers-to ma...Mehr anzeigen
Offizieller Titel
AI-Based Cancer Diagnosis and Prediction Using Electronic Health Records
Erkrankungen
TumorWeitere Studien-IDs
- Cancer
NCT-Nummer
Studienbeginn (tatsächlich)
2025-01-19
Zuletzt aktualisiert
2025-07-30
Studienende (vorauss.)
2025-10-01
Geplante Rekrutierung
1.000.000
Studientyp
Beobachtungsstudie
Status
Offene Rekrutierung
Stichwörter
tumor
Early Disease Prediction
AI-Assisted Diagnosis
Early Disease Prediction
AI-Assisted Diagnosis
Studienarme/Interventionen
| Teilnehmergruppe/Studienarm | Intervention/Behandlung |
|---|---|
Healthy Cohort This group consists of individuals without any diagnosed cancer. Participants in this cohort will serve as the control group for comparison to the experimental group. No interventions or treatments will be administered to this cohort, as they represent a baseline of healthy individuals. | AI-Based Diagnostic and Prognostic Model This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, imaging data, and genetic information, to predict the risk of cancer. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of cancer risks. By analyzing historical health data, the model aims to predict potential cancer devel...Mehr anzeigen |
Tumor Cohort This group consists of individuals diagnosed with cancer, including various types. Participants in this cohort will serve as the experimental group for evaluating the effectiveness of the early prediction model in identifying cancer risks and improving diagnostic accuracy. | AI-Based Diagnostic and Prognostic Model This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, imaging data, and genetic information, to predict the risk of cancer. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of cancer risks. By analyzing historical health data, the model aims to predict potential cancer devel...Mehr anzeigen |
Hauptergebnismessungen
Nebenergebnismessungen
| Ergebnismessung | Beschreibung der Messung | Zeitrahmen |
|---|---|---|
Area Under the Curve (AUC) | AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1). | 1 year |
F1 Score | The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall. | 1 year |
| Ergebnismessung | Beschreibung der Messung | Zeitrahmen |
|---|---|---|
Sensitivity (True Positive Rate) | Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders. | 1 year |
Specificity (True Negative Rate) | Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative. | 1 year |
Teilnahme-Assistent
Eignungskriterien
Zugelassene Altersgruppen
Kind, Erwachsene, Ältere Erwachsene
Mindestalter
0 Years
Zugelassene Geschlechter
Alle
Akzeptiert gesunde Freiwillige
Ja
1、Patients with comprehensive electronic health records (EHRs), including medical history, laboratory test results, imaging data, and genetic data (if available).
2. Individuals without severe cognitive impairments or conditions that would prevent them from providing informed consent or participating in the study.
3. Parents or guardians must provide informed consent for minors, while adult participants must provide informed consent for themselves.
- Patients with incomplete or missing key electronic health record data or insufficient follow-up data.
- Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation.
- Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).
The Eye Hospital of Wenzhou Medical UniversityVerantwortliche Partei
Kang Zhang, Hauptprüfer, Chief Scientist, Wenzhou Medical University
Zentrale Studienkontakte
Kontakt: Fei Liu, MD, +86 13810512704, [email protected]
7 Studienstandorte in 1 Ländern
Guangdong
Guangzhou Women and Children's Medical Center, Guangzhou, Guangdong, China
Bingzhou Li, MD, Kontakt, +86-0756-2222569, [email protected]
Offene Rekrutierung
Nanfang Hospital, Guangzhou, Guangdong, China
Zhuomin Li, MD, Kontakt, +86-0577-85397527, [email protected]
Offene Rekrutierung
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
Yunfang Yu, MD, Kontakt, +86 020-81332199, [email protected]
Offene Rekrutierung
Sun Yat-sen University Cancer Hospital, Guangzhou, Guangdong, China
Yuxing Lu, MD, Kontakt, +86 13161233730, [email protected]
Offene Rekrutierung
Sichuan
West China Hospital, Chengdu, Sichuan, China
Kai Wang, MD, Kontakt, +86 028-85422114, [email protected]
Offene Rekrutierung
Zhejiang
First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
Cheng Tang, MD, Kontakt, [email protected]
Offene Rekrutierung
Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
Sian Liu, MD, Kontakt, +86-0577-88002888, [email protected]
Offene Rekrutierung