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Popa SL, Surdea-Blaga T, Dumitrascu DL, Pop AV, Ismaiel A, David L, Brata VD, Turtoi DC, Chiarioni G, Savarino EV, Zsigmond I, Czako Z, Leucuta DC. Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1493. [PMID: 39336534 PMCID: PMC11434326 DOI: 10.3390/medicina60091493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Revised: 08/12/2024] [Accepted: 09/09/2024] [Indexed: 09/30/2024]
Abstract
Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks.
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Affiliation(s)
- Stefan Lucian Popa
- Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Teodora Surdea-Blaga
- Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Dan Lucian Dumitrascu
- Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Andrei Vasile Pop
- Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Abdulrahman Ismaiel
- Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Liliana David
- Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Vlad Dumitru Brata
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Daria Claudia Turtoi
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Giuseppe Chiarioni
- Il Cerchio Med Global Healthcare, Verona Center, 37100 Verona, Italy
- UNC Center for Functional GI and Motility Disorders, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Edoardo Vincenzo Savarino
- Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35128 Padova, Italy
| | - Imre Zsigmond
- Faculty of Mathematics and Computer Science, Babes-Bolyai University, 400347 Cluj-Napoca, Romania
| | - Zoltan Czako
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Daniel Corneliu Leucuta
- Department of Medical Informatics and Biostatistics, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
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2
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Anwar A, Khalifa Y, Lucatorto E, Coyle JL, Sejdic E. Towards a comprehensive bedside swallow screening protocol using cross-domain transformation and high-resolution cervical auscultation. Artif Intell Med 2024; 154:102921. [PMID: 38991399 DOI: 10.1016/j.artmed.2024.102921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 07/13/2024]
Abstract
High-resolution cervical auscultation (HRCA) is an emerging noninvasive and accessible option to assess swallowing by relying upon accelerometry and sound sensors. HRCA has shown tremendous promise and accuracy in identifying and predicting swallowing physiology and biomechanics with accuracies equivalent to trained human judges. These insights have historically been available only through instrumental swallowing evaluation methods, such as videofluoroscopy and endoscopy. HRCA uses supervised learning techniques to interpret swallowing physiology from the acquired signals, which are collected during radiographic assessment of swallowing using barium contrast. Conversely, bedside swallowing screening is typically conducted in non-radiographic settings using only water. This poses a challenge to translating and generalizing HRCA algorithms to bedside screening due to the rheological differences between barium and water. To address this gap, we proposed a cross-domain transformation framework that uses cycle generative adversarial networks to convert HRCA signals of water swallows into a domain compatible with the barium swallows-trained HRCA algorithms. The proposed framework achieved a cross-domain transformation accuracy that surpassed 90%. The authenticity of the generated signals was confirmed using a binary classifier to confirm the framework's capability to produce indistinguishable signals. This framework was also assessed for retaining swallow physiological and biomechanical properties in the signals by applying an existing model from the literature that identifies the opening and closure of the upper esophageal sphincter. The outcomes of this model showed nearly identical results between the generated and original signals. These findings suggest that the proposed transformation framework is a feasible avenue to advance HCRA towards clinical deployment for water-based swallowing screenings.
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Affiliation(s)
- Ayman Anwar
- Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.
| | - Yassin Khalifa
- Center for Research Computing, University of Pittsburgh, Pittsburgh, PA, USA; Information Technology Analytics, University of Pittsburgh, Pittsburgh, PA, USA; Systems and Biomedical Engineering, Cairo University, Giza, Egypt.
| | - Erin Lucatorto
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Ervin Sejdic
- Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada; North York General Hospital, Toronto, ON, Canada.
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Yeganeh A, Johannssen A, Chukhrova N, Rasouli M. Monitoring multistage healthcare processes using state space models and a machine learning based framework. Artif Intell Med 2024; 151:102826. [PMID: 38579438 DOI: 10.1016/j.artmed.2024.102826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 04/07/2024]
Abstract
Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.
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Affiliation(s)
- Ali Yeganeh
- University of Hamburg, 20146 Hamburg, Germany.
| | | | | | - Mohammad Rasouli
- Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
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4
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Alter IL, Chan K, Lechien J, Rameau A. An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review. Eur Arch Otorhinolaryngol 2024; 281:2723-2731. [PMID: 38393353 DOI: 10.1007/s00405-024-08512-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 01/25/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools. METHODS Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar. RESULTS Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability. CONCLUSIONS Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial. LEVEL OF EVIDENCE: 4
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Affiliation(s)
- Isaac L Alter
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Karly Chan
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Jérome Lechien
- Department of Otorhinolaryngology, Head and Neck Surgery, Hôpital Foch, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France
- Department of Human Anatomy and Experimental Oncology, Faculty of Medicine, UMONS Research Institute for Health and Sciences Technology, University of Mons (UMons), Mons, Belgium
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA.
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5
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Fass O, Rogers BD, Gyawali CP. Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility. Curr Gastroenterol Rep 2024; 26:115-123. [PMID: 38324172 PMCID: PMC10960670 DOI: 10.1007/s11894-024-00921-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2024] [Indexed: 02/08/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is a broad term that pertains to a computer's ability to mimic and sometimes surpass human intelligence in interpretation of large datasets. The adoption of AI in gastrointestinal motility has been slower compared to other areas such as polyp detection and interpretation of histopathology. RECENT FINDINGS Within esophageal physiologic testing, AI can automate interpretation of image-based tests, especially high resolution manometry (HRM) and functional luminal imaging probe (FLIP) studies. Basic tasks such as identification of landmarks, determining adequacy of the HRM study and identification from achalasia from non-achalasia patterns are achieved with good accuracy. However, existing AI systems compare AI interpretation to expert analysis rather than to clinical outcome from management based on AI diagnosis. The use of AI methods is much less advanced within the field of ambulatory reflux monitoring, where challenges exist in assimilation of data from multiple impedance and pH channels. There remains potential for replication of the AI successes within esophageal physiologic testing to HRM of the anorectum, and to innovative and novel methods of evaluating gastric electrical activity and motor function. The use of AI has tremendous potential to improve detection of dysmotility within the esophagus using esophageal physiologic testing, as well as in other regions of the gastrointestinal tract. Eventually, integration of patient presentation, demographics and alternate test results to individual motility test interpretation will improve diagnostic precision and prognostication using AI tools.
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Affiliation(s)
- Ofer Fass
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Benjamin D Rogers
- Division of Gastroenterology, Hepatology and Nutrition, University of Louisville School of Medicine, Louisville, KY, USA
- Division of Gastroenterology, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8124, Saint Louis, MO, 63110, USA
| | - C Prakash Gyawali
- Division of Gastroenterology, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8124, Saint Louis, MO, 63110, USA.
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Saraiva MM, Pouca MV, Ribeiro T, Afonso J, Cardoso H, Sousa P, Ferreira J, Macedo G, Junior IF. Artificial Intelligence and Anorectal Manometry: Automatic Detection and Differentiation of Anorectal Motility Patterns-A Proof-of-Concept Study. Clin Transl Gastroenterol 2023; 14:e00555. [PMID: 36520781 PMCID: PMC10584284 DOI: 10.14309/ctg.0000000000000555] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 11/18/2022] [Indexed: 10/20/2023] Open
Abstract
INTRODUCTION Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders, prevalent in the population. Nevertheless, the accessibility to this examination is limited, and the complexity of data analysis and report is a significant drawback. This pilot study aimed to develop and validate an artificial intelligence model to automatically differentiate motility patterns of fecal incontinence (FI) from obstructed defecation (OD) using ARM data. METHODS We developed and tested multiple machine learning algorithms for the automatic interpretation of ARM data. Four models were tested: k-nearest neighbors, support vector machines, random forests, and gradient boosting (xGB). These models were trained using a stratified 5-fold strategy. Their performance was assessed after fine-tuning of each model's hyperparameters, using 90% of data for training and 10% of data for testing. RESULTS A total of 827 ARM examinations were used in this study. After fine-tuning, the xGB model presented an overall accuracy (84.6% ± 2.9%), similar to that of random forests (82.7% ± 4.8%) and support vector machines (81.0% ± 8.0%) and higher that of k-nearest neighbors (74.4% ± 3.8%). The xGB models showed the highest discriminating performance between OD and FI, with an area under the curve of 0.939. DISCUSSION The tested machine learning algorithms, particularly the xGB model, accurately differentiated between FI and OD manometric patterns. Subsequent development of these tools may optimize the access to ARM studies, which may have a significant impact on the management of patients with anorectal functional diseases.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Maria Vila Pouca
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Pedro Sousa
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Ilario Froehner Junior
- Department of Gastrointestinal Motility, Nossa Senhora das Graças Hospital, Curitiba, Paraná, Brazil
- Department of Coloproctology, Pelvia—Gastrointestinal Motility and Continence, Curitiba, Paraná, Brazil
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Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
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Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
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Kou W, Soni P, Klug MW, Etemadi M, Kahrilas PJ, Pandolfino JE, Carlson DA. An artificial intelligence platform provides an accurate interpretation of esophageal motility from Functional Lumen Imaging Probe Panometry studies. Neurogastroenterol Motil 2023; 35:e14549. [PMID: 36808777 PMCID: PMC10272090 DOI: 10.1111/nmo.14549] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 01/18/2023] [Accepted: 01/30/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND Functional lumen imaging probe (FLIP) Panometry is performed at the time of sedated endoscopy and evaluates esophageal motility in response to distension. This study aimed to develop and test an automated artificial intelligence (AI) platform that could interpret FLIP Panometry studies. METHODS The study cohort included 678 consecutive patients and 35 asymptomatic controls that completed FLIP Panometry during endoscopy and high-resolution manometry (HRM). "True" study labels for model training and testing were assigned by experienced esophagologists per a hierarchical classification scheme. The supervised, deep learning, AI model generated FLIP Panometry heatmaps from raw FLIP data and based on convolutional neural networks assigned esophageal motility labels using a two-stage prediction model. Model performance was tested on a 15% held-out test set (n = 103); the remainder of the studies were utilized for model training (n = 610). KEY RESULTS "True" FLIP labels across the entire cohort included 190 (27%) "normal," 265 (37%) "not normal/not achalasia," and 258 (36%) "achalasia." On the test set, both the Normal/Not normal and the achalasia/not achalasia models achieved an accuracy of 89% (with 89%/88% recall, 90%/89% precision, respectively). Of 28 patients with achalasia (per HRM) in the test set, 0 were predicted as "normal" and 93% as "achalasia" by the AI model. CONCLUSIONS An AI platform provided accurate interpretation of FLIP Panometry esophageal motility studies from a single center compared with the impression of experienced FLIP Panometry interpreters. This platform may provide useful clinical decision support for esophageal motility diagnosis from FLIP Panometry studies performed at the time of endoscopy.
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Affiliation(s)
- Wenjun Kou
- Division of Gastroenterology and Hepatology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Priyanka Soni
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Matthew W. Klug
- Department of Information Services, Northwestern Medicine, Chicago, Illinois, USA
| | - Mozziyar Etemadi
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
- Department of Information Services, Northwestern Medicine, Chicago, Illinois, USA
| | - Peter J. Kahrilas
- Division of Gastroenterology and Hepatology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - John E. Pandolfino
- Division of Gastroenterology and Hepatology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Dustin A. Carlson
- Division of Gastroenterology and Hepatology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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Rafieivand S, Hassan Moradi M, Momayez Sanat Z, Asl Soleimani H. A fuzzy-based framework for diagnosing esophageal motility disorder using high-resolution manometry. J Biomed Inform 2023; 141:104355. [PMID: 37023842 DOI: 10.1016/j.jbi.2023.104355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/05/2023] [Accepted: 03/30/2023] [Indexed: 04/08/2023]
Abstract
In recent years, the high-resolution manometry (HRM) technique has been increasingly used to study esophageal and colonic pressurization and has become a standard routine for discovering mobility disorders. In addition to evolving guidelines for the interpretation of HRM like Chicago standard, some complexities, such as the dependency of normative reference values on the recording device and other external variables, still remain for medical professions. In this study, a decision support framework is developed to aid the diagnosis of esophageal mobility disorders based on HRM data. To abstract HRM data, Spearman correlation is employed to model the spatio-temporal dependencies of pressure values of HRM components and convolutional graph neural networks are then utilized to embed relation graphs to the features vector. In the decision-making stage, a novel Expert per Class Fuzzy Classifier (EPC-FC) is presented that employs the ensemble structure and contains expertized sub-classifiers for recognizing a specific disorder. Training sub-classifiers using the negative correlation learning method makes the EPC-FC highly generalizable. Meanwhile, separating the sub-classifiers of each class gives flexibility and interpretability to the structure. The suggested framework is evaluated on a dataset of 67 patients in 5 different classes recorded in Shariati Hospital. The average accuracy of 78.03% for a single swallow and 92.54% for subject-level is achieved for distinguishing mobility disorders. Moreover, compared with the other studies, the presented framework has an outstanding performance considering that it imposes no limits on the type of classes or HRM data. On the other hand, the EPC-FC outperforms other comparative classifiers such as SVM and AdaBoost not only in HRM diagnosis but also on other benchmark classification problems.
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Affiliation(s)
- Safa Rafieivand
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | | | - Zahra Momayez Sanat
- Digestive Disease Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hosein Asl Soleimani
- Digestive Disease Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
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10
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Surdea-Blaga T, Sebestyen G, Czako Z, Hangan A, Dumitrascu DL, Ismaiel A, David L, Zsigmond I, Chiarioni G, Savarino E, Leucuta DC, Popa SL. Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning. SENSORS 2022; 22:s22145227. [PMID: 35890906 PMCID: PMC9323128 DOI: 10.3390/s22145227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/04/2022] [Accepted: 07/08/2022] [Indexed: 02/04/2023]
Abstract
The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest—the precise instant of swallowing. After resizing and rescaling the photos, they were utilized as input for the Deep Learning models. The InceptionV3 Deep Learning model was used to identify the precise class of the IRP. We used the DenseNet201 CNN architecture to classify the images into 5 different classes of swallowing disorders. Finally, we combined the results of the two trained ML models to automate the Chicago Classification algorithm. With this solution we obtained a top-1 accuracy and f1-score of 86% with no human intervention, automating the whole flow, from image preprocessing until Chicago classification and diagnosis.
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Affiliation(s)
- Teodora Surdea-Blaga
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (T.S.-B.); (D.L.D.); (A.I.); (L.D.); (S.L.P.)
| | - Gheorghe Sebestyen
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (Z.C.); (A.H.)
- Correspondence:
| | - Zoltan Czako
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (Z.C.); (A.H.)
| | - Anca Hangan
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (Z.C.); (A.H.)
| | - Dan Lucian Dumitrascu
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (T.S.-B.); (D.L.D.); (A.I.); (L.D.); (S.L.P.)
| | - Abdulrahman Ismaiel
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (T.S.-B.); (D.L.D.); (A.I.); (L.D.); (S.L.P.)
| | - Liliana David
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (T.S.-B.); (D.L.D.); (A.I.); (L.D.); (S.L.P.)
| | - Imre Zsigmond
- Faculty of Mathematics and Computer Science, Babes-Bolyai University, 400347 Cluj-Napoca, Romania;
| | - Giuseppe Chiarioni
- Division of Gastroenterology, AOUI Verona, University of Verona, 37134 Verona, Italy;
| | - Edoardo Savarino
- Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35122 Padova, Italy;
| | - Daniel Corneliu Leucuta
- Department of Medical Informatics and Biostatistics, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania;
| | - Stefan Lucian Popa
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (T.S.-B.); (D.L.D.); (A.I.); (L.D.); (S.L.P.)
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