Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05497258 |
| Other study ID # |
2022K-K146(C01) |
| Secondary ID |
|
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
August 15, 2022 |
| Est. completion date |
October 1, 2022 |
Study information
| Verified date |
August 2022 |
| Source |
Renmin Hospital of Wuhan University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
This is a study to validate the effect of the intelligent diagnostic evidence-based analytic
system in acute abdominal pain augmentation. Included physicians were randomly assigned into
control or AI-assisted group. In this experiment, the whole electronic health record of each
acute abdominal pain patient was divided into two parts, signs and symptoms recording
(including chief complaint, present history, physical examination, past medical history,
trauma surgery history, personal history, family history, obstetrical history, menstrual
history, blood transfusion history, drug allergy history) and auxiliary examination recording
(including laboratory examination and radiology report). For each case, the control group
readers will first read the signs and symptoms recording of electronic health record and make
a clinical diagnosis. Then the readers have to decide to either order a list of auxiliary
examinations or confirm the clinical diagnosis without further examination. If the readers
choose to order examinations, the corresponding examination results will be feedback to the
readers, and the readers can then decide to either continue to order a list of auxiliary
examinations or make a confirming diagnosis. Such cycle will last until the reader make a
confirming diagnosis. For the AI-assisted readers, the physicians were additionally provided
with the feature extracted by IDEAS-AAP, a list of suspicious diagnoses predicted by
IDEAS-AAP, and corresponding diagnostic criteria according to guidelines. After the readers
get the examination results, the IDEAS-AAP will renew its diagnosis prediction
Description:
In recent years, with the continuous development of science and technology, the range of
diagnostic tests and biomarkers for disease and treatment modalities has increased
exponentially, and medical information has become increasingly complex. This requires the
clinician to comprehensively evaluate the patient's condition, so as to choose the best
examination and treatment. However, for the complex symptoms in the actual clinical
environment, the corresponding diseases are numerous; In the face of complex and heavy
clinical work, how to extract the important characteristics of patients' diseases faster and
more accurately to achieve high-quality and accurate diagnosis and treatment is the key
problem to be solved at present. For example, in the field of digestion, the chief complaint
of abdominal pain is one of the most common clinical symptoms of patients seeking medical
treatment, and some acute abdominal pain, such as gastrointestinal ulcer perforation,
strangulated intestinal obstruction, acute obstructive suppurative cholangitis and other
urgent onset, narrow treatment time window, high mortality. Clinicians must make a quick
diagnosis and distinguish between those that require emergency intervention and those that do
not in order to manage patients in a timely manner and avoid catastrophic events. However,
the causes of abdominal pain are many and the mechanisms are complex. In addition, since pain
is a subjective sensation and is greatly influenced by subjective factors, there are no clear
objective indicators to determine whether or not and the degree of pain, and it is extremely
challenging to correctly diagnose and interpret abdominal pain. To this end, the clinician
must take a detailed history and perform a thorough physical examination when evaluating a
patient's abdominal pain. In recent years, artificial intelligence technology has developed
rapidly, especially in the field of medicine has been widely applied research, mainly
reflected in the diagnosis and differential diagnosis of diseases, prognosis judgment and
clinical decision analysis. Some studies have shown that in terms of auxiliary pathology and
imaging diagnosis, AI has reached or even exceeded the average diagnostic level of
corresponding specialists. Most of these studies focus on pattern recognition based on
images, and the logical judgment based on natural language using medical records information
is still in the preliminary development stage. There are no relevant reports on integrating
comprehensive information of large medical records to make intelligent prediction of
digestive tract diseases.
