Congenital MicrotiaDL-FPANCT07846332
Deep Learning for Flap Perfusion Detection
A study testing AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstruction in people with Congenital Microtia, run by Jing Xu at 1 site in China.
Trial summary
The aim of this study is to develop a convolutional neural network-based image recognition model to identify flap perfusion abnormalities after first-stage auricular reconstruction in patients with congenital microtia. Read more about this study
- Who is it for?
- Ages 6 to 40 years
- Conditions the study is for
- Congenital Microtia
- What is being tested?
- AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstruction
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 232 participants
- Where
- 1 siteChina
- Study dates
- 22 Sep 2026to 31 Dec 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 6 to 40 years
- All sexes
- No healthy volunteers
- Child, Adult
Who may be able to join
- Age 6-40 years;
- Typical microtia morphology consistent with the diagnostic criteria in Chinese Plastic Surgery;
- Patients after first-stage total auricular reconstruction who have not yet undergone second-stage surgery;
- The participant or family member is able to use a smartphone.
Who cannot join
- Abnormal coagulation or other relevant laboratory findings;
- Absence of normal fascial coverage on the affected ear, or presence of eczema, infection, or other local abnormalities on the affected side;
- Severe cognitive impairment or psychiatric disorders;
- Significant dysfunction of major organs;
- The patient or family member does not consent to participation in the study.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
EYE & ENT Hospital, Fudan University
Shanghai, 201112
Jing Xu, +86 34233106, xujing10126@163.com
About this study
Official title: Application of a Deep Learning-Based Model for Detecting Flap Perfusion Abnormalities After First-Stage Auricular Reconstruction in Congenital Microtia Patients
The aim of this study is to develop a convolutional neural network-based image recognition model to identify flap perfusion abnormalities after first-stage auricular reconstruction in patients with congenital microtia. In addition, the study seeks to evaluate the real-world performance of this model when applied to clinical flap monitoring.
The main questions addressed are:
Read more
- Can an artificial intelligence (AI) image recognition model accurately detect flap perfusion abnormalities from photographs of the reconstructed ear?
- Compared with conventional clinical monitoring, does AI-based image recognition alter the incidence of flap-related complications?
- How accurate is AI-based detection of flap abnormalities, and how satisfied are patients and healthcare providers with this monitoring approach?
The study is divided into two phases.
- In phase I, investigators will collect postoperative photographs of reconstructed ears and relevant clinical data from patients with congenital microtia undergoing first-stage auricular reconstruction. Images will be annotated based on the clinical judgement of experienced healthcare professionals, and these data will be used to train a convolutional neural network image recognition model. The model's ability to identify flap perfusion abnormalities will then be assessed through internal validation.
- In phase II, the clinical performance of traditional monitoring methods will be compared with AI-based image recognition monitoring.
Patients admitted between July 2027 and December 2027 who meet the inclusion criteria will receive conventional clinical flap monitoring and routine follow-up and will comprise the control group.
Patients admitted between January 2028 and June 2028 who meet the inclusion criteria will receive flap monitoring using the convolutional neural network-based image recognition module in addition to standard clinical management and follow-up and will comprise the study group.
Investigators will compare postoperative complication rates, the accuracy of detecting flap perfusion abnormalities, and satisfaction among patients and healthcare providers between the two groups.
Participants will:
- Undergo flap monitoring after first-stage auricular reconstruction according to the clinical follow-up schedule;
- Provide, or cooperate in the acquisition of, reconstructed ear photographs and relevant clinical data as required by the study;
- Receive routine follow-up and health education;
- In the study group, undergo flap monitoring assisted by the image recognition module in addition to conventional monitoring.
- Conditions
- Congenital Microtia
- Being tested
- AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstruction
- Keywords
- Congenital microtia, first-stage auricular reconstruction, flap perfusion abnormalities, convolutional neural network, deep learning, medical image recognition
- Medical subject headings
- Congenital Microtia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstructionBehavioural
In addition to routine outpatient follow-up, an AI image recognition-based remote intelligent monitoring system led by specialist nurses will be implemented. After discharge, patients will receive daily prompts to upload photographs of the reconstructed ear. The AI system will analyse images in real time: if classified as normal, it will provide standard care recommendations; if classified as abnormal, it will automatically alert the clinical team. Specialist nurses will manually review abnormal images and, as appropriate, request repeat imaging, intensify home care and increase monitoring frequency, or report to the supervising physician and instruct the patient to return to the hospital for further management.
| Study group | Role | What they receive |
|---|---|---|
| Control group | No intervention | Patients with congenital microtia will undergo first-stage auricular reconstruction with standard flap assessment and routine follow-up, without AI-based remote flap image monitoring. |
| Intervention group | Experimental | Participants will undergo first-stage auricular reconstruction and receive AI-assisted remote flap monitoring postoperatively, with scheduled image acquisition and flap assessment conducted according to the study protocol. Receives: Behavioral: AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstruction |
What is being measured
The primary outcome is the main question the study is designed to answer.
Complication rate
Complications will be identified and recorded based on outpatient review notes and follow-up records. The complication rate will be calculated as:
Complication rate = (number of patients with complications after first-stage auricular reconstruction / total number of patients) × 100%.
The following common postoperative complications after first-stage auricular reconstruction will be included: hematoma requiring re-hemostasis, infection, flap necrosis, cartilage exposure, and deformation of the auricular framework.
Measured: Assessed 3 months postoperatively
Secondary outcomes (2)
Patient satisfaction
Patient satisfaction will be assessed using a self-designed satisfaction questionnaire comprising 15 items divided into five domains: consultation process and environment (3 items), quality of medical and nursing services (3 items), attitude of medical and nursing staff (3 items), health education (3 items), and use of the case management platform (3 items). Each item will be rated on a 3-point Likert scale from "dissatisfied" to "satisfied," scored 1-3, yielding a total score range of 15-45. The satisfaction score will be calculated as the sum of all item scores, with higher scores indicating greater patient satisfaction.
Measured: Assessed 3 months postoperatively
System complication detection rate
The system complication detection rate will be calculated as:
System complication detection rate = (number of cases classified as abnormal by the system and clinically confirmed to have auricular reconstruction complications / total number of cases classified as abnormal by the system) × 100%.
Measured: Assessed at 3 months postoperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Sep 2026
- Study start22 Sep 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Congenital MicrotiaStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 232 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jing Xu
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Jing Xu, Eye & ENT Hospital of Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07846332
- Sponsor's protocol ID
- Flap Recognition Model
- Acronym
- DL-FPA
- Status last verified
- Sep 2026
- First submitted
- 23 Sep 2026
- Last updated
- 29 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared with external researchers due to patient privacy protection and institutional data policy. Aggregated study results will be reported in publications.
Publications
Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond). 2021 Nov;41(11):1100-1115. doi: 10.1002/cac2.12215. Epub 2021 Oct 6.
PubMed 34613667Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng. 2021 Jun;5(6):555-570. doi: 10.1038/s41551-020-00682-w. Epub 2021 Mar 1.
PubMed 33649564van der Voort SR, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Nandoe Tewarie R, Lycklama GJ, De Witt Hamer PC, Eijgelaar RS, French PJ, Dubbink HJ, Vincent AJPE, Niessen WJ, van den Bent MJ, Smits M, Klein S. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning. Neuro Oncol. 2023 Feb 14;25(2):279-289. doi: 10.1093/neuonc/noac166.
PubMed 35788352Blaicher W, Pokorny H, Rockenschaub S, Puhalla H, Muhlbacher F. Organ transplantation in Austria. Ann Transplant. 1996;1(3):41-4. No abstract available.
PubMed 9869919Chen X, Wang X, Zhang K, Fung KM, Thai TC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022 Jul;79:102444. doi: 10.1016/j.media.2022.102444. Epub 2022 Apr 4.
PubMed 35472844Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med. 2024 Feb;169:107777. doi: 10.1016/j.compbiomed.2023.107777. Epub 2023 Dec 5.
PubMed 38104516Jia Z, Wang K, Li Y, Liu Z, Qin J, Yang Q. High Precision Feature Fast Extraction Strategy for Aircraft Attitude Sensor Fault Based on RepVGG and SENet Attention Mechanism. Sensors (Basel). 2022 Dec 9;22(24):9662. doi: 10.3390/s22249662.
PubMed 36560031Koshy R, Mahmood A. Optimizing Deep CNN Architectures for Face Liveness Detection. Entropy (Basel). 2019 Apr 20;21(4):423. doi: 10.3390/e21040423.
PubMed 33267137Zhang J, Xie Y, Wu Q, Xia Y. Medical image classification using synergic deep learning. Med Image Anal. 2019 May;54:10-19. doi: 10.1016/j.media.2019.02.010. Epub 2019 Feb 18.
PubMed 30818161Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55. doi: 10.1038/s41580-021-00407-0. Epub 2021 Sep 13.
PubMed 34518686Fu YY, Li CL, Zhang JL, Zhang TY. Autologous cartilage microtia reconstruction: Complications and risk factors. Int J Pediatr Otorhinolaryngol. 2019 Jan;116:1-6. doi: 10.1016/j.ijporl.2018.09.035. Epub 2018 Oct 2.
PubMed 30554677Reinisch JF, van Hovell Tot Westerflier CVA, Gould DJ, Tahiri YT. Secondary Salvage of the Unsatisfactory Microtia Reconstruction. Plast Reconstr Surg. 2020 May;145(5):1252-1261. doi: 10.1097/PRS.0000000000006766.
PubMed 32332549Li Q, Zhou X, Wang Y, Qian J, Zhang Q. Auricular reconstruction of congenital microtia by using the modified Nagata method: Personal 10-Year experience with 1350 cases. J Plast Reconstr Aesthet Surg. 2018 Oct;71(10):1462-1468. doi: 10.1016/j.bjps.2018.05.036. Epub 2018 Jun 19.
PubMed 30097399Fan Y, Liu W, Fan X, Niu X, Chen X. Psychosocial status of patients with unilateral and bilateral microtia before auricular reconstruction surgery. Int J Pediatr Otorhinolaryngol. 2021 Dec;151:110928. doi: 10.1016/j.ijporl.2021.110928. Epub 2021 Sep 25.
PubMed 34607045Chen X, Ma J, Zhang T. Genetics and Epigenetics in the Genesis and Development of Microtia. J Craniofac Surg. 2024 Feb 12;35(3):e261-6. doi: 10.1097/SCS.0000000000010004. Online ahead of print.
PubMed 38345940
Study at a glance
From the registry record, last updated by the sponsor on 29 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of a Deep Learning-Based Model for Detecting Flap Perfusion Abnormalities After First-Stage Auricular Reconstruction in Congenital Microtia Patients
- ClinicalTrials.gov ID
- NCT07846332
- Acronym / sponsor ID
- DL-FPA / Flap Recognition Model
- Conditions
- Congenital Microtia
- Keywords
- Congenital microtia, first-stage auricular reconstruction, flap perfusion abnormalities, convolutional neural network, deep learning, medical image recognition
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 232 participants (estimated)
- Lead sponsor
- Jing XuUniversity, hospital or other
- Study start
- 22 Sep 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 29 Sep 2026 (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.
The aim of this study is to develop a convolutional neural network-based image recognition model to identify flap perfusion abnormalities after first-stage auricular reconstruction in patients with congenital microtia. In addition, the study seeks to evaluate the real-world performance of this model when applied to clinical flap monitoring.
The main questions addressed are:
- Can an artificial intelligence (AI) image recognition model accurately detect flap perfusion abnormalities from photographs of the reconstructed ear?
- Compared with conventional clinical monitoring, does AI-based image recognition alter the incidence of flap-related complications?
- How accurate is AI-based detection of flap abnormalities, and how satisfied are patients and healthcare providers with this monitoring approach?
The study is divided into two phases.
- In phase I, investigators will collect postoperative photographs of reconstructed ears and relevant clinical data from patients with congenital microtia undergoing first-stage auricular reconstruction. Images will be annotated based on the clinical judgement of experienced healthcare professionals, and these data will be used to train a convolutional neural network image recognition model. The model's ability to identify flap perfusion abnormalities will then be assessed through internal validation.
- In phase II, the clinical performance of traditional monitoring methods will be compared with AI-based image recognition monitoring.
Patients admitted between July 2027 and December 2027 who meet the inclusion criteria will receive conventional clinical flap monitoring and routine follow-up and will comprise the control group.
Patients admitted between January 2028 and June 2028 who meet the inclusion criteria will receive flap monitoring using the convolutional neural network-based image recognition module in addition to standard clinical management and follow-up and will comprise the study group.
Investigators will compare postoperative complication rates, the accuracy of detecting flap perfusion abnormalities, and satisfaction among patients and healthcare providers between the two groups.
Participants will:
- Undergo flap monitoring after first-stage auricular reconstruction according to the clinical follow-up schedule;
- Provide, or cooperate in the acquisition of, reconstructed ear photographs and relevant clinical data as required by the study;
- Receive routine follow-up and health education;
- In the study group, undergo flap monitoring assisted by the image recognition module in addition to conventional monitoring.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control group | No intervention | –Patients with congenital microtia will undergo first-stage auricular reconstruction with standard flap assessment and routine follow-up, without AI-based remote flap image monitoring. |
| Intervention group | Experimental | Behavioral: AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstructionParticipants will undergo first-stage auricular reconstruction and receive AI-assisted remote flap monitoring postoperatively, with scheduled image acquisition and flap assessment conducted according to the study protocol. |
Who may qualify
- Ages
- 6 to 40 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
Key inclusion criteria (4 listed)
- Age 6-40 years;
- Typical microtia morphology consistent with the diagnostic criteria in Chinese Plastic Surgery;
- Patients after first-stage total auricular reconstruction who have not yet undergone second-stage surgery;
- The participant or family member is able to use a smartphone.
Key exclusion criteria (5 listed)
- Abnormal coagulation or other relevant laboratory findings;
- Absence of normal fascial coverage on the affected ear, or presence of eczema, infection, or other local abnormalities on the affected side;
- Severe cognitive impairment or psychiatric disorders;
- Significant dysfunction of major organs;
- The patient or family member does not consent to participation in the study.
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 |
|---|---|---|
| AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstructionIn addition to routine outpatient follow-up, an AI image recognition-based remote intelligent monitoring system led by specialist nurses will be implemented. After discharge, patients will receive daily prompts to upload photographs of the reconstructed ear. The AI system will analyse images in real time: if classified as normal, it will provide standard care recommendations; if classified as abnormal, it will automatically alert the clinical team. Specialist nurses will manually review abnormal images and, as appropriate, request repeat imaging, intensify home care and increase monitoring frequency, or report to the supervising physician and instruct the patient to return to the hospital for further management. | Behavioural | Intervention group |
Primary outcome
Complication rate
Time frame: Assessed 3 months postoperatively
Complications will be identified and recorded based on outpatient review notes and follow-up records. The complication rate will be calculated as: Complication rate = (number of patients with complications after first-stage auricular reconstruction / total number of patients) × 100%. The following common postoperative complications after first-stage auricular reconstruction will be included: hematoma requiring re-hemostasis, infection, flap necrosis, cartilage exposure, and deformation of the auricular framework.
2 secondary outcomes
Patient satisfaction
Time frame: Assessed 3 months postoperatively
System complication detection rate
Time frame: Assessed at 3 months postoperatively
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Sep 2026.
- Background: PubMed 34613667Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond). 2021 Nov;41(11):1100-1115. doi: 10.1002/cac2.12215. Epub 2021 Oct 6.
- Background: PubMed 33649564Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng. 2021 Jun;5(6):555-570. doi: 10.1038/s41551-020-00682-w. Epub 2021 Mar 1.
- Background: PubMed 35788352van der Voort SR, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Nandoe Tewarie R, Lycklama GJ, De Witt Hamer PC, Eijgelaar RS, French PJ, Dubbink HJ, Vincent AJPE, Niessen WJ, van den Bent MJ, Smits M, Klein S. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning. Neuro Oncol. 2023 Feb 14;25(2):279-289. doi: 10.1093/neuonc/noac166.
- Background: PubMed 9869919Blaicher W, Pokorny H, Rockenschaub S, Puhalla H, Muhlbacher F. Organ transplantation in Austria. Ann Transplant. 1996;1(3):41-4. No abstract available.
- Background: PubMed 35472844Chen X, Wang X, Zhang K, Fung KM, Thai TC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022 Jul;79:102444. doi: 10.1016/j.media.2022.102444. Epub 2022 Apr 4.
- Background: PubMed 38104516Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med. 2024 Feb;169:107777. doi: 10.1016/j.compbiomed.2023.107777. Epub 2023 Dec 5.
- Background: PubMed 36560031Jia Z, Wang K, Li Y, Liu Z, Qin J, Yang Q. High Precision Feature Fast Extraction Strategy for Aircraft Attitude Sensor Fault Based on RepVGG and SENet Attention Mechanism. Sensors (Basel). 2022 Dec 9;22(24):9662. doi: 10.3390/s22249662.
- Background: PubMed 33267137Koshy R, Mahmood A. Optimizing Deep CNN Architectures for Face Liveness Detection. Entropy (Basel). 2019 Apr 20;21(4):423. doi: 10.3390/e21040423.
- Background: PubMed 30818161Zhang J, Xie Y, Wu Q, Xia Y. Medical image classification using synergic deep learning. Med Image Anal. 2019 May;54:10-19. doi: 10.1016/j.media.2019.02.010. Epub 2019 Feb 18.
- Background: PubMed 34518686Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55. doi: 10.1038/s41580-021-00407-0. Epub 2021 Sep 13.
- Background: PubMed 30554677Fu YY, Li CL, Zhang JL, Zhang TY. Autologous cartilage microtia reconstruction: Complications and risk factors. Int J Pediatr Otorhinolaryngol. 2019 Jan;116:1-6. doi: 10.1016/j.ijporl.2018.09.035. Epub 2018 Oct 2.
- Background: PubMed 32332549Reinisch JF, van Hovell Tot Westerflier CVA, Gould DJ, Tahiri YT. Secondary Salvage of the Unsatisfactory Microtia Reconstruction. Plast Reconstr Surg. 2020 May;145(5):1252-1261. doi: 10.1097/PRS.0000000000006766.
- Individual participant dataSharing planned: No. Individual participant data will not be shared with external researchers due to patient privacy protection and institutional data policy. Aggregated study results will be reported in publications.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstruction in people with Congenital Microtia, run by Jing Xu at 1 site in China.
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 4 inclusion and 5 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: opening soon, as of the sponsor's last update on 29 Sep 2026. 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 China. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jiaying Gao (+86 13795315458, 1254091997@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Complication rate (time frame: Assessed 3 months postoperatively). 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 NCT07846332, which also lists 16 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 29 Sep 2026.