Análisis y descripción de la calidad de vida específica en pacientes con fístula anal

Ferrer-Márquez, Manuel; Espínola-Cortés, Natalia; Reina-Duarte, Ángel; Granero‐Molina, José; Fernández‐Sola, Cayetano; Hernández‐Padilla, José Manuel

doi:10.1016/j.ciresp.2017.12.003

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…Complex anal fistula refers to the one with 2 or more internal or external openings and fistulas [ 4 ], which will bring great inconvenience to the patient's life. Its incidence is reported to be approximately 0.01%, and the young are predominantly affected [ 5 ]. There are two main treatment methods for anal fistula: one is conservative treatment based on traditional Chinese medicine, and the other is surgical treatment [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based CT Image Characteristics and Postoperative Anal Function Restoration for Patients with Complex Anal Fistula

Han

Chen

Cheng

et al. 2021

Journal of Healthcare Engineering

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Objective. This study aimed to optimize the CT images of anal fistula patients using a convolutional neural network (CNN) algorithm to investigate the anal function recovery. Methods. 57 patients with complex anal fistulas admitted to our hospital from January 2020 to February 2021 were selected as research subjects. Of them, CT images of 34 cases were processed using the deep learning neural network, defined as the experimental group, and the remaining unprocessed 23 cases were in the control group. Whether to process CT images depended on the patient’s own wish. The imaging results were compared with the results observed during the surgery. Results. It was found that, in the experimental group, the images were clearer, with DSC = 0.89, precision = 0.98, and recall = 0.87, indicating that the processing effects were good; that the CT imaging results in the experimental group were more consistent with those observed during the surgery, and the difference was notable ( P < 0.05 ). Furthermore, the experimental group had lower RP (mmHg), AMCP (mmHg) scores, and postoperative recurrence rate, with notable differences noted ( P < 0.05 ). Conclusion. CT images processed by deep learning are clearer, leading to higher accuracy of preoperative diagnosis, which is suggested in clinics.

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Section: Introductionmentioning

confidence: 99%