Chest X-ray Interpretation: Detecting Devices and Device-Related Complications

Gambato, M.; Scotti, Nicola; Borsari, Giacomo; Bertoja, Jacopo Zambon; Gabrieli, Joseph; Cassai, Alessandro De; Cester, Giacomo; Navalesi, Paolo; Quaia, Emilio; Causin, Francesco

doi:10.3390/diagnostics13040599

Cited by 5 publications

(5 citation statements)

References 57 publications

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“…Alternatively, one could use robust PCA [132] to limit the influence of noisy data on SSAM fitting. Another complication of real chest X-ray data may connected devices that could occlude the underlying lung structure [133], which could also be removed with robust PCA as shown in face recognition literature [134].…”

Section: Limitationsmentioning

confidence: 99%

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

Williams,

Ahlqvist,

Cunningham

et al. 2024

PLoS ONE

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For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

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Section: Limitationsmentioning

confidence: 99%

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

Williams,

Ahlqvist,

Cunningham

et al. 2024

PLoS ONE

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“…Because this complication is rare and unpredictable, healthcare professionals may not be familiar with its presentation or diagnosis. 7 Recognizing the signs and symptoms early is crucial for prompt intervention and avoiding adverse outcomes. 8 , 9 …”

Section: Introductionmentioning

confidence: 99%

“…5,6 Because this complication is rare and unpredictable, healthcare professionals may not be familiar with its presentation or diagnosis. 7 Recognizing the signs and symptoms early is crucial for prompt intervention and avoiding adverse outcomes. 8,9 In acute respiratory status deterioration in the pediatric intensive care unit, equipment failure is among the most common causes.…”

Section: Introductionmentioning

confidence: 99%

Partial Obstruction of the Endotracheal Tube by a Part of the Metallic Stylet; Case Report and Review of the Literature

Assiri,

Alhelali,

AL-Benhassan

et al. 2023

IMCRJ

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Endotracheal intubation is common in the emergency department, intensive care units, and operating rooms. It involves the insertion of an endotracheal tube (ETT) through the mouth or nose into the trachea to maintain a patent airway and facilitate mechanical ventilation. Using a stylet during intubation can guide the ETT through the vocal cords. However, complications can arise when the stylet is not removed after successful intubation. Herewith, we reported a patient who was two years old and suffered from respiratory failure. However, in the first 12 hours, we observed a foreign body in the trachea, a small end of a metal stylet immediately removed by bronchoscope. This case demonstrates that multiple uses of a stylet, especially by a single user, can result in impaction of the stylet in the ETT during intubation, requiring force when the stylet is withdrawn after intubation, which can result in breakage, shearing, and retention of the stylet or plastic sheath in the lumen of the ETT.

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“…[3][4][5] Chest radiographs (CXRs) are the most commonly acquired medical images worldwide and are routinely used to confirm the correct device positioning. [6][7][8] Tracheal tube malpositioning (estimated incidence: 0.5%-7% according to Pacheco-Lopez et al 9 and Tsai et al, 10 5%-25% according to Yi et al 11 and O'Connor et al 12 ) increases the risk of accidental endobronchial intubation or accidental extubation. 13 Chest radiograph imaging after CVC insertion is critical to rule out malpositioning (estimated incidences from <1% up to >11% 14,15 ) and iatrogenic pneumothorax (estimated incidence approximately 0.5% 15 ).…”

mentioning

confidence: 99%

“…Their proper placement is crucial for patient safety and optimal clinical outcomes 3–5 . Chest radiographs (CXRs) are the most commonly acquired medical images worldwide and are routinely used to confirm the correct device positioning 6–8 . Tracheal tube malpositioning (estimated incidence: 0.5%–7% according to Pacheco-Lopez et al 9 and Tsai et al, 10 5%–25% according to Yi et al 11 and O'Connor et al 12 ) increases the risk of accidental endobronchial intubation or accidental extubation 13 .…”

mentioning

confidence: 99%

Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions

Rueckel,

Huemmer,

Shahidi

et al. 2023

Invest Radiol

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Purpose To develop and validate an artificial intelligence algorithm for the positioning assessment of tracheal tubes (TTs) and central venous catheters (CVCs) in supine chest radiographs (SCXRs) by using an algorithm approach allowing for adjustable definitions of intended device positioning. Materials and Methods Positioning quality of CVCs and TTs is evaluated by spatially correlating the respective tip positions with anatomical structures. For CVC analysis, a configurable region of interest is defined to approximate the expected region of well-positioned CVC tips from segmentations of anatomical landmarks. The CVC/TT information is estimated by introducing a new multitask neural network architecture for jointly performing type/existence classification, course segmentation, and tip detection. Validation data consisted of 589 SCXRs that have been radiologically annotated for inserted TTs/CVCs, including an experts' categorical positioning assessment (reading 1). In-image positions of algorithm-detected TT/CVC tips could be corrected using a validation software tool (reading 2) that finally allowed for localization accuracy quantification. Algorithmic detection of images with misplaced devices (reading 1 as reference standard) was quantified by receiver operating characteristics. Results Supine chest radiographs were correctly classified according to inserted TTs/CVCs in 100%/98% of the cases, thereby with high accuracy in also spatially localizing the medical device tips: corrections less than 3 mm in >86% (TTs) and 77% (CVCs) of the cases. Chest radiographs with malpositioned devices were detected with area under the curves of >0.98 (TTs), >0.96 (CVCs with accidental vessel turnover), and >0.93 (also suboptimal CVC insertion length considered). The receiver operating characteristics limitations regarding CVC assessment were mainly caused by limitations of the applied CXR position definitions (region of interest derived from anatomical landmarks), not by algorithmic spatial detection inaccuracies. Conclusions The TT and CVC tips were accurately localized in SCXRs by the presented algorithms, but triaging applications for CVC positioning assessment still suffer from the vague definition of optimal CXR positioning. Our algorithm, however, allows for an adjustment of these criteria, theoretically enabling them to meet user-specific or patient subgroups requirements. Besides CVC tip analysis, future work should also include specific course analysis for accidental vessel turnover detection.

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Chest X-ray Interpretation: Detecting Devices and Device-Related Complications

Cited by 5 publications

References 57 publications

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

Partial Obstruction of the Endotracheal Tube by a Part of the Metallic Stylet; Case Report and Review of the Literature

Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions

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