Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence.
Background: Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy. Objective: Using computed tomography (CT) colonoscopy images, this study aimed to examine the morphological features of t...
| Publicado en: | Colombia Medica Vol. 55; no. 2; pp. 1 - 15 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Universidad del Valle
6/30/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181212949&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181212949 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01208322 165D jtl: Colombia Medica issn: 01208322 maglogo: N pubinfo: dt: 6/30/2024 vid: 55 iid: 2 pid: 19876 pub: Universidad del Valle artinfo: ui: 181212949 181212949 181212949 10.25100/cm.v55i2.5944 181212949 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P db: lth ui: 181212949 tig: atl: Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence. aug: au: Sasani, Hadi Ozkan, Mazhar Simsek, Mehmet Ali Sasani, Mahmut affil: Tekirdag Namik Kemal University, Faculty of Medicine, Department of Radiology, Tekirdag, Turkey. sug: subj: Intestine, Large Radiography Colon Anatomy and Histology Colonography, Computed Tomographic Methods Radiographic Image Interpretation, Computer-Assisted Evaluation Artificial Intelligence Age Factors Human Male Female Adult Middle Age Aged Aged, 80 and Over Retrospective Design Algorithms Sex Factors Descriptive Statistics Deep Learning Disease Management Imaging, Three-Dimensional Data Analysis Software Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy. Objective: Using computed tomography (CT) colonoscopy images, this study aimed to examine the morphological features of the colon's anatomical sections and investigate the relationship of these sections with each other or with age groups. The shapes of the transverse colon were analyzed using artificial intelligence. Methods: The study was conducted as a two- and three-dimensional examination of CT colonography images of people between 40 and 80 years old, which were obtained retrospectively. An artificial intelligence algorithm (YOLOv8) was used for shape detection on 3D colon images. Results: 160 people with a mean age of 89 men and 71 women included in the study were 57.79±8.55 and 56.55±6.60, respectively, and there was no statistically significant difference (p= 0.24). The total colon length was 166.11±25.07 cm for men and 158.73±21.92 cm for women, with no significant difference between groups (p=0.12). As a result of the training of the model Precision, Recall, and mAP were found to be 0.8578, 0.7940, and 0.9142, respectively Conclusions: The study highlights the importance of understanding the type and morphology of the large intestine for accurate interpretation of CT colonography results and effective clinical management of patients with suspected large intestine abnormalities. Furthermore, this study showed that 88.57% of the images in the test data set were detected correctly and that AI can play an important role in colon typing. Antecedentes: Las propiedades morfológicas como la longitud y la tortuosidad de los segmentos del intestino grueso juegan un papel importante, especialmente en los procedimientos de intervención como la colonoscopia. Objetivo: Examinar las características morfológicas de las secciones anatómicas del colon e investigar la relación de estas secciones entre sí o con grupos de edad. Las formas del colon transverso se analizaron con inteligencia artificial. Métodos: El estudio se realizó con un examen bidimensional y tridimensional de imágenes de colonografía por tomografía computarizada de 160 personas con edades comprendidas entre 40 y 79 años obtenidas retrospectivamente. Se utilizó un algoritmo de inteligencia artificial (YOLOv8) para la detección de formas en imágenes de colon en 3D. Resultados: La edad media de 89 hombres y 71 mujeres incluidas en el estudio fue 57.79 ±8.55 y 56.55 ±6.60, respectivamente (p= 0.24). La longitud total del colon fue de 166.11 ±25.07 cm para los hombres y 158.73 ±21.92 cm para las mujeres (p= 0.12). Como resultado del entrenamiento del modelo, se encontró que Precision, Recall y mAP fueron 0.8578, 0.7940 y 0.9142, respectivamente. Conclusiones: El estudio destaca la importancia de comprender el tipo y la morfología del intestino grueso para una interpretación precisa de los resultados de la colonografía por TC y un tratamiento clínico eficaz de los pacientes con sospecha de anomalías del intestino grueso. Además, este estudio demostró que el 88.6% de las imágenes del conjunto de datos de prueba se detectaron correctamente y que la IA puede desempeñar un papel importante en la tipificación del colon. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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