Chest x-ray automated triage: A semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures

Mosquera, Candelaria; Diaz, Facundo Nahuel; Binder, Fernando; Rabellino, José Martín; Benítez, Sonia; Beresñak, Alejandro Daniel; Seehaus, Alberto; Ducrey, Gabriel; Ocantos, Jorge; Luna, Daniel

doi:10.1016/j.cmpb.2021.106130

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…Among the 63 studies, 56 studies identified pneumothorax on chest radiography [ 26 – 81 ], four studies on computed tomography [ 82 – 85 ], one study on ECG [ 86 ], one study used chest radiography and photography using a smartphone [ 87 ], and one study used chest radiography and tabular data [ 88 ]. Six studies developed and internally tuned DLs [ 37 , 52 , 63 , 67 , 74 , 76 ], 25 studies also internally tested their DLs [ 32 , 33 , 35 , 38 , 40 , 41 , 43 , 45 , 47 , 48 , 50 , 55 , 60 , 65 , 69 , 70 , 73 , 75 , 79 – 83 , 85 , 86 ] and 32 studies externally tested the DLs [ 26 – 31 , 34 , 36 , 39 , 42 , 44 , 46 , 49 , 51 , 53 , 54 , 56 – 59 , 61 , 62 , 64 , 66 , 68 , 71 , 72 , 77 , 78 , 84 , 87 , 88 ].…”

Section: Resultsmentioning

confidence: 99%

“…The proportion of participants with pneumothorax in each dataset also ranged widely (median (IQR) 17.2% (10.8–25.0%)). 23 studies did not describe the sex of the study participants [ 27 , 31 – 33 , 36 – 38 , 55 , 59 , 62 , 65 , 69 – 71 , 73 , 76 – 78 , 81 , 82 , 86 – 88 ] and 24 studies did not include age information [ 27 , 31 – 33 , 36 – 38 , 55 , 59 , 62 , 65 , 69 – 71 , 73 , 74 , 76 – 78 , 81 , 82 , 86 – 88 ]. Detailed dataset characteristics are shown in supplementary table S2 .…”

Section: Resultsmentioning

confidence: 99%

“…In studies that performed external model validation, the median dataset size was 1137 (range 175–112 120). 17 studies included localisation of pneumothorax in model output to improve end-user interpretability [ 26 – 28 , 30 – 33 , 36 – 38 , 40 , 47 , 56 , 59 , 68 , 84 , 85 ]. Detailed DL characteristics are shown in supplementary table S3 .…”

Section: Resultsmentioning

confidence: 99%

“…We extracted 89 contingency tables from 32 studies that provided sufficient information to calculate contingency tables for pneumothorax classification [ 27 – 36 , 38 , 39 , 42 – 46 , 48 , 51 , 53 – 56 , 58 , 61 , 68 , 70 , 71 , 75 , 78 , 80 , 87 ]. 68 contingency tables were extracted for reported DL performance and 21 contingency tables were extracted for physician performance.…”

Section: Resultsmentioning

confidence: 99%

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Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

Sugibayashi¹,

Walston²,

Matsumoto³

et al. 2023

Eur Respir Rev

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BackgroundDeep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.MethodsA search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysisviaa hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool.ResultsIn 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96–0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79–89%) for DL and 85% (95% CI 73–92%) for physicians and the pooled specificity was 96% (95% CI 94–98%) for DL and 98% (95% CI 95–99%) for physicians. More than half of the original studies (57%) had a high risk of bias.ConclusionsOur review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

Sugibayashi¹,

Walston²,

Matsumoto³

et al. 2023

Eur Respir Rev

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show abstract

“…In this review, only a limited number of included studies performed external validation of the evaluated device. Some studies reported significant drops in model performance when they were applied to external data [73,85]. These studies that reveal the so called 'generalization gap' underscore the need for vigilance by healthcare providers whenever efforts are made to translate machine learning models into clinical practice.…”

Section: Risk and Safetymentioning

confidence: 99%

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review

Ahmad

Milne²,

Buchlak

et al. 2023

Diagnostics

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Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems.

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Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare

Yuan,

Yu,

Xie

et al. 2024

Medicine Advances

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Machine learning (ML) has achieved substantial success in performing healthcare tasks in which the configuration of every part of the ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use ML techniques, Automated ML (AutoML) has emerged as a prospective solution. However, most models generated by AutoML are black boxes that are challenging to comprehend and deploy in healthcare settings. We conducted a systematic review to examine AutoML with interpretation systems for healthcare. We searched four databases (MEDLINE, EMBASE, Web of Science, and Scopus) complemented with seven prestigious ML conferences (AAAI, ACL, ICLR, ICML, IJCAI, KDD, and NeurIPS) that reported AutoML with interpretation for healthcare before September 1, 2023. We included 118 articles related to AutoML with interpretation in healthcare. First, we illustrated AutoML techniques used in the included publications, including automated data preparation, automated feature engineering, and automated model development, accompanied by a real‐world case study to demonstrate the advantages of AutoML over classic ML. Then, we summarized interpretation methods: feature interaction and importance, data dimensionality reduction, intrinsically interpretable models, and knowledge distillation and rule extraction. Finally, we detailed how AutoML with interpretation has been used for six major data types: image, free text, tabular data, signal, genomic sequences, and multi‐modality. To some extent, AutoML with interpretation provides effortless development and improves users' trust in ML in healthcare settings. In future studies, researchers should explore automated data preparation, seamless integration of automation and interpretation, compatibility with multi‐modality, and utilization of foundation models.

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Chest x-ray automated triage: A semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures

Cited by 11 publications

References 17 publications

Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review

Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare

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