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Набор скоро начнётся NCT07485465

Lymphoedema Diagnosis and Treatment

Наблюдательное Lymphedema Artificial Intelligence (AI)

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: a domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.
Кому может быть актуально
Состояния в реестре: Lymphedema, Artificial Intelligence (AI). Базовые параметры: от 18 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
Turkey (Türkiye)
Следующий шаг
Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
Официальное название

The Role of Chat GPT in the Diagnosis and Treatment of Lymphedema

Обзор

A domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.

Подробное описание

The differential diagnosis of lower limb swelling remains problematic in clinical practice, as lymphedema, lipedema, and peripheral venous disease often present with similar features. Therefore, we developed LymphedemaGPT, a GPT-5-based clinical assistant designed to help practitioners navigate these diagnostic complexities.

LymphedemaGPT was designed to analyze structured patient data to extract clinical summaries, present possible diagnoses with percentage probabilities, create differential diagnosis tables, suggest additional diagnostic tests, and generate evidence-based treatment plans.

LymphedemaGPT's responses are based on seven scientific publications uploaded to the system, in addition to the Sleigh BC \& Manna B (2023) and Rockson approaches. Owing to this resource integration, the model can provide more reliable and consistent recommendations aligned with evidence-based medicine principles based on current guidelines and scientific publications.

Extensive prompt engineering techniques were applied to optimize the diagnostic and therapeutic accuracy of LymphedemaGPT.

The model is programmed to prioritize the questioning phase until a diagnosis is confirmed. In the initial responses, only structured anamnesis questions were asked, and after sufficient information was collected, systematic analysis and treatment planning were initiated. The response flow was designed as follows: (1) history collection, (2) preliminary assessment, (3) additional questioning (if necessary), and (4) systematic analysis and treatment planning when sufficient data were obtained.

The following patient data was presented to LymphedemaGPT in a structured format:

Demographic data: Age, gender, height, weight Medical history: Additional illnesses, medications used, habits (smoking, alcohol) Complaint characteristics: Time of onset, affected area, symptoms (pain, heaviness, numbness, tingling, stiffness, limited movement, weakness, etc.) Physical examination findings: Stemmer sign, swelling change with elevation, skin findings Medical history: History of infection, history of surgery, history of malignancy (radiotherapy, chemotherapy, lymph node dissection, type of cancer) Imaging: Doppler ultrasonography and lymphoscintigraphy results, if available

LymphedemaGPT was asked to respond in the following 12-part standard format: (1) Clinical Summary, (2) Possible Diagnoses (% probability), (3) Differential Diagnosis, (4) Recommended Diagnostic Tests, (5) Treatment Plan, (6) Patient Education and Follow-up, (7) Red Flags, (8) references, (9) Level of Evidence and Confidence Score, (10) Ethical Note, (11) Data Summary (JSON/CSV), and (12) Analysis Timestamp.

The performance of LymphedemaGPT was evaluated by experienced physicians based on the following eight criteria:

1. Accuracy and adequacy of clinical summary 2. Accuracy of primary diagnosis 3. Accuracy of differential diagnosis table 4. Appropriateness of recommended diagnostic tests 5. Concordance of treatment plan with current guidelines 6. Appropriateness of compression class/exercise-diet recommendations 7. Adequacy of red flags 8. Overall clinical utility

Each criterion was scored using a 5-point Likert scale: 5 = excellent/completely suitable, 4 = good/significantly suitable, 3 = moderate/partially suitable, 2 = poor/inadequate, and 1 = very poor/not suitable. The maximum score for each case was 40 (8 criteria × 5 points), and the minimum score was 8.

Two experienced physicians independently performed the evaluation. The evaluators were physical medicine and rehabilitation specialists experienced in the management of lymphedema and lipedema, and they independently performed the scoring.

Вмешательства

  • Другое a domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency
    LymphedemaGPT was designed to analyze structured patient data to extract clinical summaries, present possible diagnoses with percentage probabilities, create differential diagnosis tables, suggest additional diagnostic tests, and generate evidence-based treatment plans.

Первичные конечные точки

  • Diagnostic accuracy rate [Срок оценки: 1 hour]
  • Treatment adequacy rate [Срок оценки: 1 hour]
  • Average criterion score [Срок оценки: 1 hour]
Вторичные конечные точки (1)
  • Overall performance score [Срок оценки: 1 hour]

Критерии участия

Критерии включения

  • Patients over the age of 18
  • Clinical diagnosis of lymphoedema
  • Clinical diagnosis of lipoedema
  • Clinical diagnosis of venous insufficiency

Критерии исключения

  • Lack of medical history
  • Lack of demographic data
  • Lack of clinical data and
  • Lack of imaging methods

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Только случаи

Центры проведения

Turkey (Türkiye) · 1 центр
  • Istanbul Fatih Sultan Mehmet Training and Research Hospital — Istanbul

Публикации

  • Leypold T, Lingens LF, Beier JP, Boos AM. Integrating AI in Lipedema Management: Assessing the Efficacy of GPT-4 as a Consultation Assistant. Life (Basel). 2024 May 20;14(5):646. doi: 10.3390/life14050646. PMID 38792666
  • Eldaly AS, Avila FR, Torres-Guzman RA, Maita K, Garcia JP, Serrano LP, Forte AJ. Artificial intelligence and lymphedema: State of the art. J Clin Transl Res. 2022 Jun 1;8(3):234-242. eCollection 2022 Jun 29. PMID 35813896
  • Wojcik S, Rulkiewicz A, Pruszczyk P, Lisik W, Pobozy M, Domienik-Karlowicz J. Beyond ChatGPT: What does GPT-4 add to healthcare? The dawn of a new era. Cardiol J. 2023;30(6):1018-1025. doi: 10.5603/cj.97515. Epub 2023 Oct 13. PMID 37830256
  • Mesko B. The Impact of Multimodal Large Language Models on Health Care's Future. J Med Internet Res. 2023 Nov 2;25:e52865. doi: 10.2196/52865. PMID 37917126

Идентификаторы

NCT: NCT07485465 · FSMLYMPHEDEMA

Первоисточники (государственные реестры)

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