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Detection and position evaluation of chest percutaneous drainage catheter on chest radiographs using deep learning.
Kim, Duk Ju; Nam, In Chul; Kim, Doo Ri; Kim, Jeong Jae; Hwang, Im-Kyung; Lee, Jeong Sub; Park, Sung Eun; Kim, Hyeonwoo.
Afiliación
  • Kim DJ; Department of Radiology, Jeju National University School of Medicine, Jeju Natuional University Hospital, Jeju, Republic of Korea.
  • Nam IC; Department of Radiology, Jeju National University School of Medicine, Jeju Natuional University Hospital, Jeju, Republic of Korea.
  • Kim DR; Department of Radiology, Jeju National University School of Medicine, Jeju Natuional University Hospital, Jeju, Republic of Korea.
  • Kim JJ; Department of Radiology, Jeju National University School of Medicine, Jeju Natuional University Hospital, Jeju, Republic of Korea.
  • Hwang IK; Department of Radiology, Jeju National University School of Medicine, Jeju Natuional University Hospital, Jeju, Republic of Korea.
  • Lee JS; Department of Radiology, Jeju National University School of Medicine, Jeju Natuional University Hospital, Jeju, Republic of Korea.
  • Park SE; Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea.
  • Kim H; Upstage AI, Yongin-si, Gyeonggi-do, Republic of Korea.
PLoS One ; 19(8): e0305859, 2024.
Article en En | MEDLINE | ID: mdl-39133733
ABSTRACT

PURPOSE:

This study aimed to develop an algorithm for the automatic detecting chest percutaneous catheter drainage (PCD) and evaluating catheter positions on chest radiographs using deep learning.

METHODS:

This retrospective study included 1,217 chest radiographs (proper positioned 937; malpositioned 280) from a total of 960 patients underwent chest PCD from October 2017 to February 2023. The tip location of the chest PCD was annotated using bounding boxes and classified as proper positioned and malpositioned. The radiographs were randomly allocated into the training, validation sets (total 1,094 radiographs; proper positioned 853 radiographs; malpositioned 241 radiographs), and test datasets (total 123 radiographs; proper positioned 84 radiographs; malpositioned 39 radiographs). The selected AI model was used to detect the catheter tip of chest PCD and evaluate the catheter's position using the test dataset to distinguish between properly positioned and malpositioned cases. Its performance in detecting the catheter and assessing its position on chest radiographs was evaluated by per radiographs and per instances. The association between the position and function of the catheter during chest PCD was evaluated.

RESULTS:

In per chest radiographs, the selected model's accuracy was 0.88. The sensitivity and specificity were 0.86 and 0.92, respectively. In per instance, the selected model's the mean Average Precision 50 (mAP50) was 0.86. The precision and recall were 0.90 and 0.79 respectively. Regarding the association between the position and function of the catheter during chest PCD, its sensitivity and specificity were 0.93 and 0.95, respectively.

CONCLUSION:

The artificial intelligence model for the automatic detection and evaluation of catheter position during chest PCD on chest radiographs demonstrated acceptable diagnostic performance and could assist radiologists and clinicians in the early detection of catheter malposition and malfunction during chest percutaneous catheter drainage.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiografía Torácica / Drenaje / Aprendizaje Profundo Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiografía Torácica / Drenaje / Aprendizaje Profundo Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos