Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia
Aim: to demonstrate an artificial intelligence model that optimises the differential diagnosis of achalasia.Material and methods. The study included 75 patients: 52 % men (mean age 44.5 ± 17.8 years) and 48 % women (mean age 45.6 ± 16.6 years,) with a preliminary diagnosis of achalasia. Patients wer...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Gastro LLC
2024-12-01
|
| Series: | Российский журнал гастроэнтерологии, гепатологии, колопроктологии |
| Subjects: | |
| Online Access: | https://www.gastro-j.ru/jour/article/view/1164 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823860036104880128 |
|---|---|
| author | O. A. Storonova N. I. Kanevskii A. S. Trukhmanov V. T. Ivashkin |
| author_facet | O. A. Storonova N. I. Kanevskii A. S. Trukhmanov V. T. Ivashkin |
| author_sort | O. A. Storonova |
| collection | DOAJ |
| description | Aim: to demonstrate an artificial intelligence model that optimises the differential diagnosis of achalasia.Material and methods. The study included 75 patients: 52 % men (mean age 44.5 ± 17.8 years) and 48 % women (mean age 45.6 ± 16.6 years,) with a preliminary diagnosis of achalasia. Patients were divided into four groups: type I, II, III achalasia and a group of patients whose results did not correspond to a diagnosis of achalasia according to HRM performed based on Chicago Classification version 4.0. On the basis of a set of data from 750 swallows and therefore 6750 manometric parameters, the artificial intelligence models DecisionTreeClassifier, RandomForestClassifier and CatBoostClassifier have been trained to provide a manometric diagnosis. The comparison criteria were the training time and the f1_score metric. The technical characteristics of the model (hyperparameters) were selected using the GridSearchCV method. The model with the best results was integrated into a web application.Results. The RandomForestClassifier was chosen as the best performing model to compare. Its technical characteristics were “decision trees” and branching depth the number of which was 14 and 5 respectively. With a maximum possible value of 1.0, these hyperparameters achieved f1_score=0.91 in 27 seconds. The web application, developed on the basis of this model, is capable of analyzing manometric data and establishing one of three types of achalasia in patients. Alternatively, it can exclude the diagnosis of achalasia. The output of an image corresponding to the diagnosis is produced for each manometric type of the disease.Conclusions. For the first time in Russia, a machine learning model based on high-resolution esophageal manometry data was developed at the V. Kh. Vasilenko Clinic of Internal Disease Propedeutics, Gastroenterology, and Hepatology of Sechenov University. The model has been applied to the creation of a web application which has the ability to substantiate the manometry diagnosis of patients. The Federal Service for Intellectual Property (Rospatent) issued a certificate of state registration of the computer program No. 2024665795 dated July 5, 2024. This artificial intelligence programme can be used in clinical practice as a medical decision support tool to optimize the process of differential diagnosis of achalasia and early detection of the disease, to determine the patient's prognosis and to select the method of further treatment. |
| format | Article |
| id | doaj-art-3bdaa6d4aeae4b3ab239d1cc3be2fd68 |
| institution | Kabale University |
| issn | 1382-4376 2658-6673 |
| language | Russian |
| publishDate | 2024-12-01 |
| publisher | Gastro LLC |
| record_format | Article |
| series | Российский журнал гастроэнтерологии, гепатологии, колопроктологии |
| spelling | doaj-art-3bdaa6d4aeae4b3ab239d1cc3be2fd682025-02-10T16:14:40ZrusGastro LLCРоссийский журнал гастроэнтерологии, гепатологии, колопроктологии1382-43762658-66732024-12-01345323910.22416/1382-4376-2024-34-5-32-391086Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal AchalasiaO. A. Storonova0N. I. Kanevskii1A. S. Trukhmanov2V. T. Ivashkin3I.M. Sechenov First Moscow State Medical University (Sechenov University)I.M. Sechenov First Moscow State Medical University (Sechenov University)I.M. Sechenov First Moscow State Medical University (Sechenov University)I.M. Sechenov First Moscow State Medical University (Sechenov University)Aim: to demonstrate an artificial intelligence model that optimises the differential diagnosis of achalasia.Material and methods. The study included 75 patients: 52 % men (mean age 44.5 ± 17.8 years) and 48 % women (mean age 45.6 ± 16.6 years,) with a preliminary diagnosis of achalasia. Patients were divided into four groups: type I, II, III achalasia and a group of patients whose results did not correspond to a diagnosis of achalasia according to HRM performed based on Chicago Classification version 4.0. On the basis of a set of data from 750 swallows and therefore 6750 manometric parameters, the artificial intelligence models DecisionTreeClassifier, RandomForestClassifier and CatBoostClassifier have been trained to provide a manometric diagnosis. The comparison criteria were the training time and the f1_score metric. The technical characteristics of the model (hyperparameters) were selected using the GridSearchCV method. The model with the best results was integrated into a web application.Results. The RandomForestClassifier was chosen as the best performing model to compare. Its technical characteristics were “decision trees” and branching depth the number of which was 14 and 5 respectively. With a maximum possible value of 1.0, these hyperparameters achieved f1_score=0.91 in 27 seconds. The web application, developed on the basis of this model, is capable of analyzing manometric data and establishing one of three types of achalasia in patients. Alternatively, it can exclude the diagnosis of achalasia. The output of an image corresponding to the diagnosis is produced for each manometric type of the disease.Conclusions. For the first time in Russia, a machine learning model based on high-resolution esophageal manometry data was developed at the V. Kh. Vasilenko Clinic of Internal Disease Propedeutics, Gastroenterology, and Hepatology of Sechenov University. The model has been applied to the creation of a web application which has the ability to substantiate the manometry diagnosis of patients. The Federal Service for Intellectual Property (Rospatent) issued a certificate of state registration of the computer program No. 2024665795 dated July 5, 2024. This artificial intelligence programme can be used in clinical practice as a medical decision support tool to optimize the process of differential diagnosis of achalasia and early detection of the disease, to determine the patient's prognosis and to select the method of further treatment.https://www.gastro-j.ru/jour/article/view/1164machine learningartificial intelligenceachalasiahigh-resolution esophageal manometryfunctional diagnostics |
| spellingShingle | O. A. Storonova N. I. Kanevskii A. S. Trukhmanov V. T. Ivashkin Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia Российский журнал гастроэнтерологии, гепатологии, колопроктологии machine learning artificial intelligence achalasia high-resolution esophageal manometry functional diagnostics |
| title | Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia |
| title_full | Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia |
| title_fullStr | Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia |
| title_full_unstemmed | Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia |
| title_short | Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia |
| title_sort | own experience in the use of artificial intelligence technologies in the diagnosis of esophageal achalasia |
| topic | machine learning artificial intelligence achalasia high-resolution esophageal manometry functional diagnostics |
| url | https://www.gastro-j.ru/jour/article/view/1164 |
| work_keys_str_mv | AT oastoronova ownexperienceintheuseofartificialintelligencetechnologiesinthediagnosisofesophagealachalasia AT nikanevskii ownexperienceintheuseofartificialintelligencetechnologiesinthediagnosisofesophagealachalasia AT astrukhmanov ownexperienceintheuseofartificialintelligencetechnologiesinthediagnosisofesophagealachalasia AT vtivashkin ownexperienceintheuseofartificialintelligencetechnologiesinthediagnosisofesophagealachalasia |