Automatic gauze tracking in laparoscopic surgery using image texture analysis

López, Eusebio de la Fuente; García, Álvaro Muñoz; Blanco, Lidia Santos del; Fraile, Juan-Carlos; Pérez-Turiel, Javier

doi:10.1016/j.cmpb.2020.105378

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…The evaluations of the gauze detection model are outlined as follows. We evaluated the gauze detection models with a test set, including 200 gauze patches and 200 background patches from the Mendeley dataset 34 . EfficientNet B3 and B5 exhibited high performance for every metric (Supplementary Table S1 ).…”

Section: Resultsmentioning

confidence: 99%

“…Swish was used as the activation function instead of a rectifier linear unit (ReLU) in EfficientNet 33 . To train the gauze detection algorithm, we used the Mendeley dataset 34 , created in a laparoscopic simulator using animal internal organs. It comprises 2935 background tissue blocks, and 1070 gauze tiles presented in three states: clean, stained, and soaked.…”

Section: Methodsmentioning

confidence: 99%

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Automated deep learning model for estimating intraoperative blood loss using gauze images

Yoon,

Yoo,

Kim

et al. 2024

Sci Rep

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The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze’s overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automated deep learning model for estimating intraoperative blood loss using gauze images

Yoon,

Yoo,

Kim

et al. 2024

Sci Rep

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“…With the aid of technology, many researchers are working on deep learning techniques suitable for image detection and classification [27]. Owing to their capability of extracting image features at different levels of hierarchy, convolutional neural networks (CNNs) Table 3 Machine learning methods key findings, prominent advantages, and disadvantages are crucial to image processing tasks [28].…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review

Abo-Zahhad,

El-Malek,

Sayed

et al. 2024

BioData Mining

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Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients’ bodies after surgery, which can lead to severe consequences such as infections and death. The repercussions highlight the critical need to address this issue. Machine learning (ML) and deep learning (DL) have displayed considerable potential for enhancing the prevention of RSIs through heightened precision and decreased reliance on human involvement. ML techniques are finding an expanding number of applications in medicine, ranging from automated imaging analysis to diagnosis. DL has enabled substantial advances in the prediction capabilities of computers by combining the availability of massive volumes of data with extremely effective learning algorithms. This paper reviews and evaluates recently published articles on the application of ML and DL in RSIs prevention and diagnosis, stressing the need for a multi-layered approach that leverages each method’s strengths to mitigate RSI risks. It highlights the key findings, advantages, and limitations of the different techniques used. Extensive datasets for training ML and DL models could enhance RSI detection systems. This paper also discusses the various datasets used by researchers for training the models. In addition, future directions for improving these technologies for RSI diagnosis and prevention are considered. By merging ML and DL with current procedures, it is conceivable to substantially minimize RSIs, enhance patient safety, and elevate surgical care standards.

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“…Furthermore, the application of CNN algorithms has played a revolutionary role in computer science and the development of deep learning, making it possible to design a detection architecture with higher accuracy than before 15 . The use of CNN in surgical AI tasks mainly includes tool recognition and detection, 1 phase recognition, 15 action recognition, 16 and gauze recognition, 17 among which the research on tool recognition and detection has still attracted more attention. Following the M2CAI 2016 Tool Presence Detection Challenge benchmark, 18 most tool detection approaches are framework‐level detection.…”

Section: Related Workmentioning

confidence: 99%

Error analysis driven network modification for surgical tools detection in laparoscopic frames

Yin

Wang

et al. 2022

Int J Imaging Syst Tech

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In minimally invasive laparoscopic surgery, accurate detection of the location and specific category of surgical tools assists the surgeon in making a correct objective judgment of the current surgical outcome and alerts to these possible adverse medical events. In this paper, an analyzing error method is proposed for revealing the specific sources of error in previous work. Then we confirm the main bottlenecks of the current methods with a high level of missing error and localization error. To reduce these errors, we render the backbone with a three‐dimensional attention mechanism and adopt a double‐headed detection head design to replace the single detection head. Moreover, we propose an enhanced multilayer perceptron called Mconv to enhance the localization branch of the double‐headed detection head. Our experiments evaluate our improved approach by our proposed error analysis method on a pubic dataset m2cai16‐tool‐locations, showing that our method yield remarkable detection accuracy than others.

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Automatic gauze tracking in laparoscopic surgery using image texture analysis

Cited by 11 publications

References 22 publications

Automated deep learning model for estimating intraoperative blood loss using gauze images

Automated deep learning model for estimating intraoperative blood loss using gauze images

Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review

Error analysis driven network modification for surgical tools detection in laparoscopic frames

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