Thyroid NoduleAIA-CEUD-TNNCT05982821
Artificial Intelligence-assisted Contrast-enhanced Ultrasound Diagnosis of Thyroid Nodules
A study testing Artificial Intelligence in people with Thyroid Nodule, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial summary
The goal of this intervention study is to learn about the diagnostic performance of artificial intelligence to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules. The main questions it aims to answer are: 1. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Thyroid Nodule
- What is being tested?
- Artificial Intelligence
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,955 participants
- Where
- 1 siteChina
- Study dates
- 3 Jun 2023to 16 Mar 2026
- Phase
- Interventional
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional ultrasound;
- Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and fine-needle aspiration biopsy;
- Patients with a final benign or malignant pathological results.
Who cannot join
- Patients with cytopathology of Bethesda I, III, or IV and without final benign or malignant pathology；
- Patients with a history of thyroid ablation or surgery;
- Patients with low-quality ultrasound images.
Trial locations and contacts
1 site in 1 country.
China1 site
Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Guangzhou, Guangdong, 510289
About this study
Official title: A Multicenter Study of Artificial Intelligence-assisted Contrast-enhanced Ultrasound Diagnosis of Thyroid Nodules
The goal of this intervention study is to learn about the diagnostic performance of artificial intelligence to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules. The main questions it aims to answer are:
- Can we develop an artificial intelligence model to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules?
- Can artificial intelligent reduce the unnecessary biopsy of thyroid nodules?
Participants will be asked to undergo contrast-enhanced ultrasound examination and ultrasound-guided fine-needle aspiration of thyroid nodules. Researchers will calculate the diagnostic performance of the contrast-enhanced ultrasound diagnosis of thyroid nodules with and without artificial intelligent assistance.
- Conditions
- Thyroid Nodule
- Being tested
- Artificial Intelligence
- Keywords
- Artificial interligence, Contrast media, diagnosis, Thyroid nodule, Ultrasonography
- Medical subject headings
- Thyroid Nodule, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial IntelligenceOther
Artificial intelligence assisted contrast-enhanced ultrasound diagnosis of thyroid nodules.
| Study group | Role | What they receive |
|---|---|---|
| Test set | Other | Participants with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2021 and December 2022 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Houjie Hospital of Dongguan and Affiliated Huadu Hospital of Southern Medical University. Receives: Other: Artificial Intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance
Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the curve
Measured: First week after primary completion date.
The necessity of biopsy
Unnecessary biopsy rate
Measured: First week after primary completion date.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2023
- Study start3 Jun 2023
- Primary completion2 Mar 2026
- Study completion16 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (19)Papers the sponsor has linked to this study.
- Other trials for Thyroid NoduleStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- 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
- Not applicable
- Intervention model
- Single group
- Model description
- A deep-learning based artificial intelligence model.
- Masking
- None (open label)
- Enrolment
- 1,955 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Jingliang Ruan, PhD, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05982821
- Sponsor's protocol ID
- SYSKY-2023-702-01
- Acronym
- AIA-CEUD-TN
- Status last verified
- Mar 2026
- First submitted
- 24 Jul 2023
- Last updated
- 25 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Data generated or analyzed during the study are available from the principal investigator by request.
Publications
Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM, Schlumberger M, Schuff KG, Sherman SI, Sosa JA, Steward DL, Tuttle RM, Wartofsky L. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid. 2016 Jan;26(1):1-133. doi: 10.1089/thy.2015.0020.
PubMed 26462967Burgos N, Ospina NS, Sipos JA. The Future of Thyroid Nodule Risk Stratification. Endocrinol Metab Clin North Am. 2022 Jun;51(2):305-321. doi: 10.1016/j.ecl.2021.12.002. Epub 2022 May 4.
PubMed 35662443Zhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, Li J, Qian L, Cui L, Chen W, Wen C, Peng Y, Chen Q, Lu M, Chen M, Wu R, Zhou W, Xue E, Li Y, Yang L, Mi C, Zhang R, Wu G, Du G, Huang D, Zhan W; Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association; Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. 2020 Chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the C-TIRADS. Endocrine. 2020 Nov;70(2):256-279. doi: 10.1007/s12020-020-02441-y. Epub 2020 Aug 21.
PubMed 32827126Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, Cronan JJ, Beland MD, Desser TS, Frates MC, Hammers LW, Hamper UM, Langer JE, Reading CC, Scoutt LM, Stavros AT. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017 May;14(5):587-595. doi: 10.1016/j.jacr.2017.01.046. Epub 2017 Apr 2.
PubMed 28372962Russ G, Bonnema SJ, Erdogan MF, Durante C, Ngu R, Leenhardt L. European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EU-TIRADS. Eur Thyroid J. 2017 Sep;6(5):225-237. doi: 10.1159/000478927. Epub 2017 Aug 8.
PubMed 29167761Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, Lim HK, Moon WJ, Na DG, Park JS, Choi YJ, Hahn SY, Jeon SJ, Jung SL, Kim DW, Kim EK, Kwak JY, Lee CY, Lee HJ, Lee JH, Lee JH, Lee KH, Park SW, Sung JY; Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology. Ultrasonography Diagnosis and Imaging-Based Management of Thyroid Nodules: Revised Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Korean J Radiol. 2016 May-Jun;17(3):370-95. doi: 10.3348/kjr.2016.17.3.370. Epub 2016 Apr 14.
PubMed 27134526Kwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, Jung HK, Choi JS, Kim BM, Kim EK. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology. 2011 Sep;260(3):892-9. doi: 10.1148/radiol.11110206. Epub 2011 Jul 19.
PubMed 21771959Burgos N, Zhao J, Brito JP, Hoang JK, Pitoia F, Maraka S, Castro MR, Lee JH, Singh Ospina N. Clinician Agreement on the Classification of Thyroid Nodules Ultrasound Features: A Survey of 2 Endocrine Societies. J Clin Endocrinol Metab. 2022 Jul 14;107(8):e3288-e3294. doi: 10.1210/clinem/dgac279.
PubMed 35521676Sidhu PS, Cantisani V, Dietrich CF, Gilja OH, Saftoiu A, Bartels E, Bertolotto M, Calliada F, Clevert DA, Cosgrove D, Deganello A, D'Onofrio M, Drudi FM, Freeman S, Harvey C, Jenssen C, Jung EM, Klauser AS, Lassau N, Meloni MF, Leen E, Nicolau C, Nolsoe C, Piscaglia F, Prada F, Prosch H, Radzina M, Savelli L, Weskott HP, Wijkstra H. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version). Ultraschall Med. 2018 Apr;39(2):e2-e44. doi: 10.1055/a-0586-1107. Epub 2018 Mar 6.
PubMed 29510439Zhang Y, Zhou P, Tian SM, Zhao YF, Li JL, Li L. Usefulness of combined use of contrast-enhanced ultrasound and TI-RADS classification for the differentiation of benign from malignant lesions of thyroid nodules. Eur Radiol. 2017 Apr;27(4):1527-1536. doi: 10.1007/s00330-016-4508-y. Epub 2016 Aug 15.
PubMed 27525973Tang C, Fang K, Guo Y, Li R, Fan X, Chen P, Chen Z, Liu Q, Zou Y. Safety of Sulfur Hexafluoride Microbubbles in Sonography of Abdominal and Superficial Organs: Retrospective Analysis of 30,222 Cases. J Ultrasound Med. 2017 Mar;36(3):531-538. doi: 10.7863/ultra.15.11075. Epub 2017 Jan 10.
PubMed 28072475Seifert P, Gorges R, Zimny M, Kreissl MC, Schenke S. Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules. Endocrine. 2020 Jan;67(1):143-154. doi: 10.1007/s12020-019-02134-1. Epub 2019 Nov 18.
PubMed 31741167Wildman-Tobriner B, Buda M, Hoang JK, Middleton WD, Thayer D, Short RG, Tessler FN, Mazurowski MA. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Radiology. 2019 Jul;292(1):112-119. doi: 10.1148/radiol.2019182128. Epub 2019 May 21.
PubMed 31112088Zhang B, Tian J, Pei S, Chen Y, He X, Dong Y, Zhang L, Mo X, Huang W, Cong S, Zhang S. Machine Learning-Assisted System for Thyroid Nodule Diagnosis. Thyroid. 2019 Jun;29(6):858-867. doi: 10.1089/thy.2018.0380. Epub 2019 Apr 27.
PubMed 30929637Peng S, Liu Y, Lv W, Liu L, Zhou Q, Yang H, Ren J, Liu G, Wang X, Zhang X, Du Q, Nie F, Huang G, Guo Y, Li J, Liang J, Hu H, Xiao H, Liu Z, Lai F, Zheng Q, Wang H, Li Y, Alexander EK, Wang W, Xiao H. Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study. Lancet Digit Health. 2021 Apr;3(4):e250-e259. doi: 10.1016/S2589-7500(21)00041-8.
PubMed 33766289
Study at a glance
From the registry record, last updated by the sponsor on 25 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicenter Study of Artificial Intelligence-assisted Contrast-enhanced Ultrasound Diagnosis of Thyroid Nodules
- ClinicalTrials.gov ID
- NCT05982821
- Acronym / sponsor ID
- AIA-CEUD-TN / SYSKY-2023-702-01
- Conditions
- Thyroid Nodule
- Keywords
- Artificial interligence, Contrast media, diagnosis, Thyroid nodule, Ultrasonography
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,955 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 3 Jun 2023 (actual)
- Primary completion
- 2 Mar 2026 (actual)
- Study completion
- 16 Mar 2026 (actual)
- First posted
- 9 Aug 2023 (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 goal of this intervention study is to learn about the diagnostic performance of artificial intelligence to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules. The main questions it aims to answer are:
- Can we develop an artificial intelligence model to assist the contrast-enhanced ultrasound diagnosis of thyroid nodules?
- Can artificial intelligent reduce the unnecessary biopsy of thyroid nodules?
Participants will be asked to undergo contrast-enhanced ultrasound examination and ultrasound-guided fine-needle aspiration of thyroid nodules. Researchers will calculate the diagnostic performance of the contrast-enhanced ultrasound diagnosis of thyroid nodules with and without artificial intelligent assistance.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- A deep-learning based artificial intelligence model.
| Arm | Type | Receives |
|---|---|---|
| Test set | Other | Other: Artificial IntelligenceParticipants with thyroid nodules underwent contrast-enhanced ultrasound and ultrasound-guided fine-needle aspiration during January 2021 and December 2022 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Houjie Hospital of Dongguan and Affiliated Huadu Hospital of Southern Medical University. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional ultrasound;
- Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and fine-needle aspiration biopsy;
- Patients with a final benign or malignant pathological results.
Key exclusion criteria (3 listed)
- Patients with cytopathology of Bethesda I, III, or IV and without final benign or malignant pathology；
- Patients with a history of thyroid ablation or surgery;
- Patients with low-quality ultrasound images.
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 |
|---|---|---|
| Artificial IntelligenceArtificial intelligence assisted contrast-enhanced ultrasound diagnosis of thyroid nodules. | Other | Test set |
Primary outcome
Diagnostic performance
Time frame: First week after primary completion date.
Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the curve
The necessity of biopsy
Time frame: First week after primary completion date.
Unnecessary biopsy rate
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 25 Mar 2026.
- Background: PubMed 26462967Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM, Schlumberger M, Schuff KG, Sherman SI, Sosa JA, Steward DL, Tuttle RM, Wartofsky L. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid. 2016 Jan;26(1):1-133. doi: 10.1089/thy.2015.0020.
- Background: PubMed 35662443Burgos N, Ospina NS, Sipos JA. The Future of Thyroid Nodule Risk Stratification. Endocrinol Metab Clin North Am. 2022 Jun;51(2):305-321. doi: 10.1016/j.ecl.2021.12.002. Epub 2022 May 4.
- Background: PubMed 32827126Zhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, Li J, Qian L, Cui L, Chen W, Wen C, Peng Y, Chen Q, Lu M, Chen M, Wu R, Zhou W, Xue E, Li Y, Yang L, Mi C, Zhang R, Wu G, Du G, Huang D, Zhan W; Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association; Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. 2020 Chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the C-TIRADS. Endocrine. 2020 Nov;70(2):256-279. doi: 10.1007/s12020-020-02441-y. Epub 2020 Aug 21.
- Background: PubMed 28372962Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, Cronan JJ, Beland MD, Desser TS, Frates MC, Hammers LW, Hamper UM, Langer JE, Reading CC, Scoutt LM, Stavros AT. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017 May;14(5):587-595. doi: 10.1016/j.jacr.2017.01.046. Epub 2017 Apr 2.
- Background: PubMed 29167761Russ G, Bonnema SJ, Erdogan MF, Durante C, Ngu R, Leenhardt L. European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EU-TIRADS. Eur Thyroid J. 2017 Sep;6(5):225-237. doi: 10.1159/000478927. Epub 2017 Aug 8.
- Background: PubMed 27134526Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, Lim HK, Moon WJ, Na DG, Park JS, Choi YJ, Hahn SY, Jeon SJ, Jung SL, Kim DW, Kim EK, Kwak JY, Lee CY, Lee HJ, Lee JH, Lee JH, Lee KH, Park SW, Sung JY; Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology. Ultrasonography Diagnosis and Imaging-Based Management of Thyroid Nodules: Revised Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Korean J Radiol. 2016 May-Jun;17(3):370-95. doi: 10.3348/kjr.2016.17.3.370. Epub 2016 Apr 14.
- Background: PubMed 21771959Kwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, Jung HK, Choi JS, Kim BM, Kim EK. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology. 2011 Sep;260(3):892-9. doi: 10.1148/radiol.11110206. Epub 2011 Jul 19.
- Background: PubMed 35521676Burgos N, Zhao J, Brito JP, Hoang JK, Pitoia F, Maraka S, Castro MR, Lee JH, Singh Ospina N. Clinician Agreement on the Classification of Thyroid Nodules Ultrasound Features: A Survey of 2 Endocrine Societies. J Clin Endocrinol Metab. 2022 Jul 14;107(8):e3288-e3294. doi: 10.1210/clinem/dgac279.
- Background: PubMed 29510439Sidhu PS, Cantisani V, Dietrich CF, Gilja OH, Saftoiu A, Bartels E, Bertolotto M, Calliada F, Clevert DA, Cosgrove D, Deganello A, D'Onofrio M, Drudi FM, Freeman S, Harvey C, Jenssen C, Jung EM, Klauser AS, Lassau N, Meloni MF, Leen E, Nicolau C, Nolsoe C, Piscaglia F, Prada F, Prosch H, Radzina M, Savelli L, Weskott HP, Wijkstra H. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version). Ultraschall Med. 2018 Apr;39(2):e2-e44. doi: 10.1055/a-0586-1107. Epub 2018 Mar 6.
- Background: PubMed 27525973Zhang Y, Zhou P, Tian SM, Zhao YF, Li JL, Li L. Usefulness of combined use of contrast-enhanced ultrasound and TI-RADS classification for the differentiation of benign from malignant lesions of thyroid nodules. Eur Radiol. 2017 Apr;27(4):1527-1536. doi: 10.1007/s00330-016-4508-y. Epub 2016 Aug 15.
- Background: PubMed 28072475Tang C, Fang K, Guo Y, Li R, Fan X, Chen P, Chen Z, Liu Q, Zou Y. Safety of Sulfur Hexafluoride Microbubbles in Sonography of Abdominal and Superficial Organs: Retrospective Analysis of 30,222 Cases. J Ultrasound Med. 2017 Mar;36(3):531-538. doi: 10.7863/ultra.15.11075. Epub 2017 Jan 10.
- Background: PubMed 31741167Seifert P, Gorges R, Zimny M, Kreissl MC, Schenke S. Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules. Endocrine. 2020 Jan;67(1):143-154. doi: 10.1007/s12020-019-02134-1. Epub 2019 Nov 18.
- Individual participant dataSharing planned: Yes. Data generated or analyzed during the study are available from the principal investigator by request.
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Thyroid Nodule
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.
Jingliang Ruan, PhD principal investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence in people with Thyroid Nodule, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University 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 3 inclusion and 3 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 25 Mar 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?
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?
Diagnostic performance (time frame: First week after primary completion date.). 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 NCT05982821, which also lists 19 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 25 Mar 2026.