Analysis of chest wall elevation after the Nuss procedure using 3D body scanning technique in patients with pectus excavatum

Uemura, Sadashige; Yoshida, Atsushi; Kuyama, Hisako

doi:10.1007/s00383-021-04885-3

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…However, the mechanisms and characteristics of chest wall re-depression after BR remain unclear ( 5 ). Because chest wall re-depression is multifactorial and spatial, clinically available methods or modalities to evaluate and analyze chest wall re-depression are needed ( 19 , 20 ). However, comprehensive evaluations of chest wall depression are difficult ( 15 , 19 , 21 ).…”

Section: Discussionmentioning

confidence: 99%

“…Because chest wall re-depression is multifactorial and spatial, clinically available methods or modalities to evaluate and analyze chest wall re-depression are needed ( 19 , 20 ). However, comprehensive evaluations of chest wall depression are difficult ( 15 , 19 , 21 ). We examined patterns of chest wall re-depression after BR and identified risk factors for recurrence.…”

Section: Discussionmentioning

confidence: 99%

“…The details of appropriate surgical techniques have achieved a wide consensus among surgeons even if they have minor variations ( 4 , 8 , 22 ). However, the most important criterion, the degree of repair remains undefined ( 19 ). Sufficient elevation of the depressed chest wall is essential to prevent recurrence of PE because RPI values after MIRPE is the most important factor for recurrence ( 5 ).…”

Section: Discussionmentioning

confidence: 99%

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Prediction of postoperative final degree and recurrence of pectus excavatum using machine learning algorithms

Hyun,

Kim,

Choi

et al. 2024

J Thorac Dis

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Background Chest wall re-depression after bar removal (BR) in pectus excavatum (PE) is insufficiently investigated. However, it is not easy to investigate chest wall re-depression due to its multifactorial characteristics. Herein, we investigated chest wall re-depression after BR using machine learning algorithms. To the best of my knowledge, this is the first study of chest wall re-depression after BR using machine learning algorithms. Methods We retrospectively reviewed 199 consecutive subjects who underwent both minimally invasive repair of pectus excavatum (MIRPE) and BR at a single hospital from March 2012 to June 2020. We investigated attributes of chest wall re-depression and risk factors for recurrence after BR, predicted final degree and recurrence of PE after BR, and suggested the optimal age at the time of MIRPE based on recurrence. Data for the chest wall re-depression were analyzed to discover differences according to age group [<10 years (early repair group; EG) vs. ≥10 years (late repair group; LG)]. Results We observed no significant difference between the Haller index and radiographical pectus index (RPI) (P=0.431) and a significant correlation between Haller index and RPI (P<0.001). RPI significantly increased for the first 6 months after BR in both age groups (both P<0.001) and was maintained at 1 year after BR. RPI value of the LG were significantly higher than those of the EG for the entire period after MIRPE (P=0.041). Recurrence of PE in the LG was significantly more frequent than in the EG (P<0.001). RPI values before and after MIRPE and age group were identified as independent risk factors for recurrence after BR (P<0.001, P=0.007, and P=0.001, respectively). The linear regression model outperformed for final RPI with performance scores of mean squared error 0.198, root mean squared error 0.445, mean absolute error 0.336, and R2 0.415. In addition, the logistic regression model outperformed for predicting recurrence with performance scores of 0.865 the area under the curve, 0.884 accuracy, 0.859 F1, 0.865 precision, and 0.884 recall. Conclusions The present study shows that machine learning algorithms can provide good estimates for postoperative results in PE. An approach integrating machine learning models and readily available clinical data can be used to create other models in the thoracic surgery field.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prediction of postoperative final degree and recurrence of pectus excavatum using machine learning algorithms

Hyun,

Kim,

Choi

et al. 2024

J Thorac Dis

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“…Song et al (2020) studied the efficacy of the compression brace on a sample of 63 patients, evaluating severity before treatment and after 2 weeks of the compression brace usage, observing a statistically significant improvement of the pathology severity, which was also confirmed by Wong et al (2014) on a sample of 50 patients. On the other hand, the evaluation of efficacy for surgical treatments was studied in the cases of thoracic bar insertion for PE correction (Gomes-Fonseca et al , 2017; Port et al , 2019; Uemura et al , 2021) and surgical PC correction (Port et al , 2018). A particular case may be the one presented by Gomes-Fonseca et al (2017) who evaluated the chest changes over time after the surgical removal of the corrective bar.…”

Section: Reverse Engineering and Additive Manufacturing-based Innovat...mentioning

confidence: 99%

Assessment and treatment of pectus deformities: a review of reverse engineering and 3D printing techniques

Mussi

Servi

Facchini

et al. 2022

RPJ

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Purpose Among thoracic malformations, pectus deformities have the highest incidence and can result in a wide range of severe and mild clinical manifestations. Recently, the treatment of pectus deformities is shifting from traditional approaches toward customized solutions. This occurs by leveraging innovative rapid prototyping tools that allow for the design and fabrication of patient-specific treatments and medical devices. This paper aims to provide a comprehensive view of the growing literature in this area to analyze the progress made in this direction. Design/methodology/approach The search was performed on major search engines through keywords inherent to reverse engineering (RE) and additive manufacturing (AM) technologies applied to pectus deformities and related treatments, selecting 54 papers. These were analyzed according to the addressed pathology, the hardware and software tools used and/or implemented and their integration within the clinical pathway. Findings First, the analysis led to analyze and divide the papers according to how RE and AM technologies are applied for surgical and non-surgical treatments, pathological assessment and preoperative simulation and planning. Second, all papers were considered within the typical rapid prototyping framework consisting of the three phases of three-dimensional (3D) scanning, 3D modelling and 3D printing. Originality/value To the best of the authors’ knowledge, to date, no survey has provided a comprehensive view of innovative and personalized treatment strategies for thoracic malformations; the present work fills this gap, allowing researchers in this field to have access to the most promising findings on the treatment and evaluation of pathology.

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“…Alternative measurement techniques have been described in PE. For example, Uemura and colleagues 6 used superimposed 3D images from pre- and postoperative images to measure the external sternal angle and elevating distance in both the axial and sagittal planes. Measurements such as 3D volumetric or area calculation may provide more accurate assessments of the changes in the shape and depth of the deformity, which in turn may correlate better than a linear measurement with the actual postprocedural cosmetic effect.…”

mentioning

confidence: 99%

Commentary: How best to quantify an uplifting pectus operation?

Chang

Hsin

2021

JTCVS Techniques

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Analysis of chest wall elevation after the Nuss procedure using 3D body scanning technique in patients with pectus excavatum

Cited by 4 publications

References 28 publications

Prediction of postoperative final degree and recurrence of pectus excavatum using machine learning algorithms

Prediction of postoperative final degree and recurrence of pectus excavatum using machine learning algorithms

Assessment and treatment of pectus deformities: a review of reverse engineering and 3D printing techniques

Commentary: How best to quantify an uplifting pectus operation?

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