Deep Learning–based Approach to Predict Pulmonary Function at                     Chest CT

Park, Hyunjung; Yun, Jihye; Lee, Sang Min; Hwang, Hye Jeon; Seo, Joon Beom; Jung, Young Ju; Hwang, Jae Cheol; Lee, Se Hee; Lee, Sei Won; Kim, Namkug

doi:10.1148/radiol.221488

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…The supplementary literature search identified 48 eligible studies ( Fig. 1 ) [ 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 ]. Table 6 shows the count of studies that addressed the four value elements provided by AI.…”

Section: Resultsmentioning

confidence: 99%

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Jhang,

Park,

Sul

et al. 2024

Korean J Radiol

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Objective This study aims to explore the opinions on the insurance coverage of artificial intelligence (AI), as categorized based on the distinct value elements offered by AI, with a specific focus on patient-centered outcomes (PCOs). PCOs are distinguished from traditional clinical outcomes and focus on patient-reported experiences and values such as quality of life, functionality, well-being, physical or emotional status, and convenience. Materials and Methods We classified the value elements provided by AI into four dimensions: clinical outcomes, economic aspects, organizational aspects, and non-clinical PCOs. The survey comprised three sections: 1) experiences with PCOs in evaluating AI, 2) opinions on the coverage of AI by the National Health Insurance of the Republic of Korea when AI demonstrated benefits across the four value elements, and 3) respondent characteristics. The opinions regarding AI insurance coverage were assessed dichotomously and semi-quantitatively: non-approval (0) vs. approval (on a 1–10 weight scale, with 10 indicating the strongest approval). The survey was conducted from July 4 to 26, 2023, using a web-based method. Responses to PCOs and other value elements were compared. Results Among 200 respondents, 44 (22%) were patients/patient representatives, 64 (32%) were industry/developers, 60 (30%) were medical practitioners/doctors, and 32 (16%) were government health personnel. The level of experience with PCOs regarding AI was low, with only 7% (14/200) having direct experience and 10% (20/200) having any experience (either direct or indirect). The approval rate for insurance coverage for PCOs was 74% (148/200), significantly lower than the corresponding rates for other value elements (82.5%–93.5%; P ≤ 0.034). The approval strength was significantly lower for PCOs, with a mean weight ± standard deviation of 5.1 ± 3.5, compared to other value elements ( P ≤ 0.036). Conclusion There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs.

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Section: Resultsmentioning

confidence: 99%

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Jhang,

Park,

Sul

et al. 2024

Korean J Radiol

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“…The development of deep learning (DL) technology has had a profound impact in the field of medical image analysis [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Through its powerful data processing and feature recognition capabilities, deep learning can automatically extract and learn valuable information from complex MRI data, recognizing subtle patterns and variations that might be overlooked by human experts, providing new opportunities for the automated analysis of complex MRI data [31,32].…”

Section: Introductionmentioning

confidence: 99%

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network

Du,

Wang,

et al. 2024

Bioengineering

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In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

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“…Because CT could provide high-resolution details of the lungs, it is regarded the gold standard for diagnosing SSc-ILD [10]. In previous research, quantitative biomarkers have been extracted from chest CT images of SSc patients, which correlate with PFTs [11]. Therefore, when PFTs are not possible and CT scans have been made for SSc patients, it is of great interest to see if CT could be used to estimate PFT.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, we are the first to estimate PFTs for SSc patients, as we found no previous works that estimate PFTs for this patient group. The most relevant and recent works on automatic estimation of PFTs from chest CT using deep learning [5], [11] are for other diseases, in which the structure-function is most likely different. Choi, et al [5] developed a network to estimate FEV 1 and FVC for patients before their first lung cancer surgery.…”

Section: Introductionmentioning

confidence: 99%

“…Their network consisted of a ResNet-50 for feature extraction and a bidirectional long short-term memory (BiLSTM) network for PFT prediction. Park, et al [11] trained two separate I3D networks to estimate FEV 1 and FVC, respectively, for subjects at risk of lung cancer. It is unclear if their models could be applied directly to SSc patients.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Pulmonary Function Estimation From Chest CT Scans Using Deep Regression Neural Networks: The Relation Between Structure and Function in Systemic Sclerosis

Jia,

Marges,

De Vries-Bouwstra

et al. 2023

IEEE Access

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Pulmonary function tests (PFTs) play an important role in screening and following-up pulmonary involvement in systemic sclerosis (SSc). However, some patients are not able to perform PFTs due to contraindications. In addition, it is unclear how lung function is affected by changes in lung structure in SSc. Therefore, this study aims to explore the potential of automatically estimating PFT results from chest CT scans of SSc patients and how different regions influence the estimation of PFTs. Deep regression networks were developed with transfer learning to estimate PFTs from 316 SSc patients. Segmented lungs and vessels were used to mask the CT images to train the network with different inputs: from entire CT scan, lungs-only to vessels-only. The network trained on entire CT scans with transfer learning achieved an ICC of 0.71, 0.76, 0.80, and 0.81 for the estimation of DLCO, FEV 1 , FVC and TLC, respectively. The performance of the networks gradually decreased when trained on data from lungs-only and vessels-only. Regression attention maps showed that regions close to large vessels were highlighted more than other regions, and occasionally regions outside the lungs were highlighted. These experiments show that apart from the lungs and large vessels, other regions contribute to PFT estimation. In addition, adding manually designed biomarkers increased the correlation (R) from 0.75, 0.74, 0.82, and 0.83 to 0.81, 0.83, 0.88, and 0.90, respectively. This suggests that that manually designed imaging biomarkers can still contribute to explaining the relation between lung function and structure.

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Deep Learning–based Approach to Predict Pulmonary Function at Chest CT

Cited by 12 publications

References 38 publications

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network

Automatic Pulmonary Function Estimation From Chest CT Scans Using Deep Regression Neural Networks: The Relation Between Structure and Function in Systemic Sclerosis

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