• 융합신호처리학회논문지
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• 제24권3호
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• pp.134-139
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• 2023

재활환자는 수술 치료 후 신속한 사회복귀를 목적으로 신체적 기능 회복을 위하여 통원치료 및 일상에서 재활운동을 수행한다. 병원에서 전문 치료사의 도움으로 운동을 수행하는 것과 달리 일상에서 환자 스스로 재활운동을 수행하는 것은 많은 어려움이 있다. 본 논문에서는 일상에서 환자 스스로 효율적이고 올바른 자세로 재활운동을 수행할 수 있도록 CNN-LSTM 기반의 상지 재활운동 실시간 모니터링 시스템을 제안한다. 제안한 시스템은 EMG, IMU가 탑재된 어깨 착용형 하드웨어를 통해 생체신호를 계측하고 학습을 위한 전처리 과정과 정규화를 진행하여 학습 데이터세트로 사용하였다. 구현된 모델은 특징 검출을 위한 3개 합성곱 레이어 3개의 폴링 레이어, 분류를 위한 2개의 LSTM 레이어로 구성되어 있으며 검증 데이터에 대한 학습 결과 97.44%를 확인할 수 있었다. 이후 Teachable machine과의 비교평가를 진행하였으며 비교평가 결과 구현된 모델은 93.6%, Teachable machine은 94.4%로 두 모델이 유사한 분류 성능을 나타내는 것을 확인하였다.

• Journal of Yeungnam Medical Science
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• 제40권4호
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• pp.388-393
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• 2023

Background: Differentiating between bacterial and nonbacterial colitis remains a challenge. We aimed to evaluate the value of serum procalcitonin (PCT) and C-reactive protein (CRP) in differentiating between bacterial and nonbacterial colitis. Methods: Adult patients with three or more episodes of watery diarrhea and colitis symptoms within 14 days of a hospital visit were eligible for this study. The patients' stool pathogen polymerase chain reaction (PCR) testing results, serum PCT levels, and serum CRP levels were analyzed retrospectively. Patients were divided into bacterial and nonbacterial colitis groups according to their PCR. The laboratory data were compared between the two groups. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: In total, 636 patients were included; 186 in the bacterial colitis group and 450 in the nonbacterial colitis group. In the bacterial colitis group, Clostridium perfringens was the commonest pathogen (n=70), followed by Clostridium difficile toxin B (n=60). The AUC for PCT and CRP was 0.557 and 0.567, respectively, indicating poor discrimination. The sensitivity and specificity for diagnosing bacterial colitis were 54.8% and 52.6% for PCT, and 52.2% and 54.2% for CRP, respectively. Combining PCT and CRP measurements did not increase the discrimination performance (AUC, 0.522; 95% confidence interval, 0.474-0.571). Conclusion: Neither PCT nor CRP helped discriminate bacterial colitis from nonbacterial colitis.

• 대한임상검사과학회지
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• 제55권4호
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• pp.314-323
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• 2023

본 연구는 한국 16개 시·도 1차·2차 의료기관의 임상병리사의 근로실태와 임금수준을 분석하여 희망임금 가이드라인을 제시하고자 한다. 전국 16개 시·도 1차·2차 의료기관의 임상병리사 1,327명을 대상으로 2022년 8월 1일부터 2022년 9월 30일 까지 구조화된 구글 설문지를 활용한 설문조사를 실시하였다. 연구 결과, 임금수준은 성별, 연령, 학력, 경력, 직책, 근무지역, 고용형태에 따라 차이가 있는 것으로 나타났다. 경력 1년 미만의 성별, 지역별에 따른 현재의 임금과 희망 임금은 차이가 있으며, 임금의 격차는 상대적으로 여성이 크게 나타났다. 임금만족도, 업무성과에 적합한 임금 및 보상인식, 기술가치 대비 보상에 대한 인식도는 각각 2.01, 2.23, 2.30으로 낮게 나타났다. 본 연구는 1차·2차 의료기관이 임상병리사가 안정적인 일자리와 업무를 제공받을 수 있는 환경을 조성하기 위해 임상병리사 업무에 대한 합당한 임금을 제공해야 하고, 대한임상병리사협회는 1차·2차 의료기관의 임상병리사의 초임임금은 3,400만원의 희망 급여를 받을 수 있도록 협력체계를 구축해야 함을 시사한다.

• 대한영상의학회지
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• 제83권3호
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• pp.620-631
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• 2022

목적 유방암 환자의 수술 전 자기공명영상에서 림프절 전이를 시사하는 소견들에 따른 진단 성적을 알아보고자 한다. 대상과 방법 수술 전 유방 자기공명영상을 촬영하고 유방암 수술을 시행한 192명의 환자를 후향적으로 분석하였다. 영상 소견에서 림프절의 크기와 장경/단경의 비율, 피질의 두께와 모양, 변연, 수질의 소실, 비대칭성, T2 강조영상에서의 신호강도, 이른 조영증강의 정도, 조영증강의 역학을 조사하였다. 수신자판단특성곡선 분석, 카이 분석과 t-검정, 맥니마 검정을 이용하여 통계분석을 시행하였다. 결과 단경의 증가, 피질의 불규칙한 모양과 피질 두께의 증가, 수질의 소실, 비대칭성, 피질의 불규칙한 변연 그리고 T2 강조영상에서의 낮은 신호강도는 전이를 시사하는 의미 있는 소견이었다. 이중 단경과 피질의 두께에 대해 수신자판단특성곡선 분석으로 각각 8.05 mm와 2.75 mm로 절단값을 얻었다. 2.75 mm 이상의 피질 두께, 피질의 불규칙한 모양은 맥니마 검정으로 다른 소견들과 비교할 때 민감도의 유의한 차이를 보였다. 피질의 불규칙한 변연(100%)은 가장 높은 특이도를 보였다. 결론 유방 자기공명영상의 전이 림프절 분석에서 2.75 mm 이상의 피질 두께와 피질의 불규칙한 모양은 다른 소견들보다 높은 민감도를 보이고 피질의 불규칙한 변연은 가장 높은 특이도를 보이는 소견이다.

• Korean Journal of Radiology
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• 제24권5호
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• pp.395-405
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• 2023

Objective: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

• Korean Journal of Radiology
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• 제24권2호
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• pp.133-144
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• 2023

Objective: Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas. Materials and Methods: Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation. Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72-0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60-0.88), 87.8%, 80.0%, and 58.1%, respectively. Conclusion: The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.

• Korean Journal of Radiology
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• 제23권10호
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• pp.949-958
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• 2022

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

• Korean Journal of Radiology
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• 제23권12호
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• pp.1251-1259
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• 2022

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

• Korean Journal of Radiology
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• 제20권8호
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• pp.1275-1284
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• 2019

Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.

• 한국방사선학회논문지
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• 제18권3호
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• pp.213-222
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• 2024

본 연구는 폐 방사선 치료를 위한 컴퓨터 단층촬영의 관전압, 관전류 조건에 따라 딥 러닝과 아틀라스기반 자동분할 방법에 따른 생성된 볼륨과 Dice 유사도 계수와 95% 하우스도르프 거리를 분석하였다. 첫 번째 결과로 관전압 관전 류의 변화에 생성된 볼륨의 결과에서는 아틀라스기반인 smart segmentation 방법이 가장 적은 볼륨 변화를 보여주었으며, 딥 러닝을 사용한 Aview RT ACS와 OncoStudio에서는 100 mAs보다 낮은 관전류에서는 볼륨이 작아지는 걸 확인했다. 두 번째 결과인 Dice 유사도 계수에서는 Aview RT ACS가 OncoStuido 보다 2% 높은 결과를 보여주고 있으며, 95% 하우스도르프거리 결과에서도 Aview RT ACS가 OncoStudio 보다 평균 0.2~0.5% 높게 분석되었다. 하지만 관전류와 관전압에 따라 각각의 결과의 표준편차에서는 오히려 OncoStudio가 낮으므로 볼륨의 변화에서도 일관성 있을 거라 사료된다. 따라서 폐 방사선 치료를 위한 CT 촬영조건에서 낮은 관전압과 낮은 관전류에서 딥 러닝 기반 자동분할 프로그램을 사용할 때는 주의가 필요하며, 일정 관전압, 관전류 이상에서 기존에 사용하고 있는 아틀라스기반 자동분할 프로그램과 유사한 결과를 도출할 수 있었다.