44 research outputs found
μλͺ© x-μ μ νμ©ν λ―Έμμ λμ¬μ± 골 μ§ν μ§λ¨ λ₯λ¬λ λͺ¨λΈ ꡬμΆ
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μκ³Όλν μνκ³Ό, 2022. 8. κΉμ΄κ²½.Background: Metabolic bone disease (MBD) of prematurity is an important complication of prematurity and accurate diagnosis and timely intervention should be made for preterm infants.
Objective: To develop a diagnostic tool for MBD of prematurity via deep learning by using wrist x-rays of preterm infants.
Methods: Study enrolled preterm infants whose birth weight was less than 1500g born at Seoul National University Childrenβs Hospital and admitted to Neonatal Intensive Care Unit from 2010 to 2020. Demographic and clinical information as well as wrist x-rays taken between 4-8 weeks of postnatal age were collected retrospectively. Two types of regions of interests (βROI 0β and βROI 1β) were annotated for deep learning model training. Demographic and clinical data was analyzed to determine the factors associated with MBD of prematurity, thus evaluating the representativeness of our study population. Wrist x-ray images were used to train and develop a diagnostic model via various deep learning algorithms, including AlexNet, DenseNet-121, ResNet-50, ResNext-50, VGG-19, CheXNet, and EfficientNet-b3.
Results: Fourteen percent (116/814) of enrolled patients were diagnosed with MBD of prematurity between 4-8 weeks of postnatal age. Analysis of clinical information revealed that birth weight less than 1000g (82.8% vs. 37.5%, p<0.001), gestational age less than 28 weeks (75.0% vs. 29.5%, p<0.001), parenteral nutrition longer than or equal to 28 days (49.1% vs, 12.0%, p<0.001) were statistically significant risk factors of MBD of prematurity. These risk factors concurred with renowned risk factors of MBD, suggesting that our population could represent general preterm population and our ground truth is reliable. Deep learning models developed by EfficientNet-b3 and VGG-19 using βROI 0β appeared to show the best quality of performance demonstrated by highest F1-score (0.844 for both models) and AUROC (0.962 for EfficientNet-b3 and 0.968 for VGG-19). βROI 0β EfficientNet-b3 model and VGG-19 model both showed sensitivity of 0.907, specificity of 0.924, positive predictive value of 0.790, negative predictive value of 0.969, and accuracy of 0.915.
Conclusion: Novel deep learning models to diagnose MBD of prematurity have been developed as a result. Our models showed sensitivity of 0.907, specificity of 0.924, and accuracy of 0.915. If applied to clinical settings, it would assist clinicians, especially for those who are novice, to detect MBD more accurately and conveniently, thereby enabling timely management to treat and prevent disease progression for preterm infants.μλ‘ : λ―Έμμ λμ¬μ± 골μ§νμ λ―Έμμκ° κ²ͺλ μ€μν ν©λ³μ¦ μ€ νλλ‘ μ νν μ§λ¨ λ° μ μ ν μμ μμμ μΉλ£μ κ°μ
μ΄ νμν μ§νμ΄λ€.
λͺ©μ : λ³Έ μ°κ΅¬λ λ―Έμμ λμ¬μ± 골μ§νμ μ§λ¨μ μ©μ΄νκ² νκ³ μ μλͺ© x-ray μμ μ 보λ₯Ό λ°νμΌλ‘ λ―Έμμ λμ¬μ± 골μ§ν μ§λ¨ λ₯λ¬λ λͺ¨λΈμ ꡬμΆνκ³ μ νλ€.
λ°©λ²: 2010λ
λΆν° 2020λ
μ¬μ΄μ μμΈλνκ΅ μ΄λ¦°μ΄λ³μμμ 1500g λ―Έλ§μΌλ‘ μΆμν λ―Έμμλ€ μ€ μ μμμ€νμμ€μ μ
μ€ν νμλ€μ λμμΌλ‘ μ°κ΅¬κ° μ§νλμλ€. μΈκ΅¬νμ μ 보, μμ μ 보, μν 4-8μ£Ό μ¬μ΄μ 촬μλ μλͺ© x-ray μμλ€μνν₯μ μΌλ‘ μμ§λμλ€. λ₯λ¬λ λͺ¨λΈ νμ΅μ μν΄ λ κ°μ§ κ΄μ¬ μμ (βROI 0βκ³Ό βROI 1β)μ μ΄λ
Έν
μ΄μ
μ΄ μλ£λμλ€. μμμ 보λ λ―Έμμ λμ¬μ± 골μ§νκ³Ό μ°κ΄λ μΈμλ€μ λΆμνκ³ μ μ¬μ©λμκ³ , μ΄λ₯Ό ν΅ν΄ μ°κ΅¬ λͺ¨μ§λ¨μ λνμ±μ νμΈνκ³ μ νμλ€. μμ§λ μλͺ© x-ray μμμ λ₯λ¬λμ ν΅ν μ§λ¨ νλ‘κ·Έλ¨μ κ°λ°νκΈ° μν νμ΅λ°μ΄ν°λ‘ μ¬μ©λμλ€. νλ‘κ·Έλ¨ κ°λ°μ μν΄ AlexNet, DenseNet-121, ResNet-50, ResNext-50, VGG-19, CheXNet, EfficientNet-b3 λ₯λ¬λ architecture κ° μ¬μ©λμλ€.
κ²°κ³Ό: λͺ¨μ§λ¨ μ€ 14.3% (116/814)κ° μν 4-8μ£Ό μ¬μ΄μ λ―Έμμ λμ¬μ± 골μ§νμΌλ‘ μ§λ¨λμλ€. μν 4-8μ£Ό μ΄λ΄μ ν λ²μ΄λΌλ μλͺ© μμμμ λμ¬μ± 골μ§νμΌλ‘ μ§λ¨λ κ²½μ°μ κ·Έλ μ§ μμ κ²½μ°λ₯Ό λ κ΅°μΌλ‘ λΉκ΅νμκ³ , μΆμμ²΄μ€ 1000g λ―Έλ§ (82.8% vs. 37.5%, p=0.000), μ¬νμ£Όμ 28μ£Ό λ―Έλ§ (75.0% vs. 29.5%, p=0.000), μ λ§₯μμ κ³΅κΈ κΈ°κ° 28μΌ μ΄μ (49.1% vs, 12.0%, p=0.000)μ΄ μ§νμ κ²ͺμ κ΅°μμ μ μλ―Ένκ² λμ λΉλμμ΄ νμΈλμ΄, λμ¬μ± 골μ§νμ μνμΈμλ‘ νμΈλμλ€. μ΄λ μ΄λ―Έ μ μλ €μ§ λ―Έμμ λμ¬μ± 골μ§νμ μνμΈμμ μΌμΉνλ©°, μ΄λ₯Ό ν΅ν΄ λͺ¨μ§λ¨μ΄ μΌλ°μ μΈ λ―Έμμ μ§λ¨μ λνν μ μμμ νμΈνμλ€. λλΆμ΄ νμ΅μ μ¬μ©λ ground truthμ μ λ’°λ λν μ
μ¦ν μ μμλ€. βROI 0βμ μ΄μ©νμ¬ EfficientNet-b3μ VGG-19λ₯Ό ν΅ν΄ κ°λ°ν μ§λ¨ λͺ¨λΈμ΄ κ°μ₯ λ°μ΄λ μ±λ₯μ λνλ΄λ©°, μ΅λκ°μ F1 μ€μ½μ΄ (0.844)μ AUROC κ° (EfficientNet-b3: 0.962, VGG-19: 0.968)μ 보μλ€., λ λͺ¨λΈμ λ―Όκ°λλ 0.907, νΉμ΄λλ 0.924, μμ± μμΈ‘λλ 0.790, μμ± μμΈ‘λλ 0.969, μ νλλ 0.915μλ€.
κ²°λ‘ : λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ λ―Έμμ λμ¬μ± 골μ§ν μ§λ¨μ μν λ₯λ¬λ λͺ¨λΈμ΄ κ°λ°λμκ³ λ―Όκ°λλ 0.907, νΉμ΄λλ 0.924, μ νλλ 0.915μ΄λ€. ν₯νμ μ΄λ¬ν μ§λ¨κΈ°λ²μ΄ μ€μ μμμ μ μ©λλ€λ©΄, νΉνλ μμκ²½λ ₯μ΄ μ μ μμμμ κ²½μ°μλ μ§νμ μ§λ¨μ΄ μ ννκ³ κ°νΈνκ² μ΄λ£¨μ΄μ§ μ μμ κ²μΌλ‘ μκ°νλ©°, μ΄λ₯Ό ν΅ν΄ μΉλ£ λ° μλ°©μ μν μ μ ν κ°μ
μ΄ κ°λ₯ν΄μ§ κ²μΌλ‘ κΈ°λνλ€.Introduction 1
Material and methods 3
Results 8
Discussion 12
Conclusion 15
References 27
Abstract in Korean 29μ
μκ³΅κ° μ£Όμμ§μ€μ κ°λ μ΄μ€ νλ¦ νλμΈμ μ κ²½λ§
νμλ
Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :곡과λν μ»΄ν¨ν°κ³΅νλΆ,2019. 8. μ νμ.μ€λλ νλ°ν μ¬μΈ΅ μ κ²½λ§ μ°κ΅¬μ λ°μ΄ν° μ μ₯ λ° μ²λ¦¬ κΈ°μ λ°λ¬λ‘ μΈν΄ μ΄ λ―Έμ§ λΏλ§ μλλΌ λΉλμ€μ κ°μ μκ° νλ¦μ κ°μ§ λμ©λ λ°μ΄ν°μμ λ€μν μΈμ λ¬Έμ λ₯Ό μννλ μ°κ΅¬κ° λμ± λ λ§μ κ΄μ¬μ λ°κ³ μλ€. κ·Έ μ€μμλ μ΄μ€ νλ¦ μ κ²½λ§μ μ²μμΌλ‘ μ κ²½λ§μ ν΅ν νμ΅μ΄ κΈ°μ‘΄μ μμμ
μΌλ‘ λ½μ νΉμ§λ³΄λ€ (hand- crafted features) μ’μ μ±λ₯μ 보μ¬μ€ μ΄νλ‘, λΉλμ€ νλ μΈμμμ μ£Όλ₯ μν€ν
μ³λ‘ μ리μ‘μλ€. λ³Έ λ
Όλ¬Έμμλ ν΄λΉ μν€ν
μ³λ₯Ό νμ₯νμ¬ λΉλμ€μμ λμ μΈμμ μν΄ λ
립μ μΌλ‘ νλ ¨λ μ΄μ€ νλ¦ μ κ²½λ§μ μκ³΅κ° μ£Όμμ§μ€μ μ£Όλ μν€ν
μ³λ₯Ό μ μν λ€. λ³Έ λ
Όλ¬Έμμλ cross attentionμ ν΅ν΄ κΈ°μ‘΄μ λ
립μ μΈ μ κ²½λ§μ μνΈ λ³΄μμ μΈ νμ΅μΌλ‘ μ±λ₯ ν₯μμ μ λνλ€. HMDB-51μ νμ€ λΉλμ€ νλμΈμ λ²€μΉ λ§ν¬μμ λ³Έ λ
Όλ¬Έμ μν€ν
μ³μ μ±λ₯μ μ€ννμμΌλ©°, κΈ°μ‘΄μ μν€ν
μ³λ³΄λ€ κ°μ λ μ±λ₯μ μ»μ μ μμλ€.Two-stream architecture has been mainstream since the success of [1], but two important information is processed independently and not interacted until the late fusion. We investigate a different spatio-temporal attention architecture based on two separate recognition streams (spatial and temporal), which interact with each other by cross attention. The spatial stream performs action recognition from still video frames, whilst the temporal stream is trained to recognise action from motion in the form of dense optical flow. Both streams convey their learned knowledge to the other stream in the form of attention maps. Cross attentions allow us to exploit the availability of supplemental information and enhance learning of the streams. To demonstrate the benefits of our proposed cross-stream spatio-temporal attention architecture, it has been evaluated on two standard action recognition benchmarks where it boosts the previous performance.μ μ½
μ 1 μ₯ μλ‘
μ 2 μ₯ κ΄λ ¨ μ°κ΅¬
2.1 νλ μΈμμμμ μ΄μ€ νλ¦ μ κ²½λ§
2.2 νλμΈμμμμ μ£Όμ μ§μ€(Attention)
μ 3 μ₯ μκ³΅κ° μ£Όμμ§μ€μ κ°λ μ΄μ€ νλ¦ νλμΈμ μ κ²½λ§
3.1 ν¨κ³Όμ μΈ μ£Όμμ§μ€ μΆμΆ
3.2 νλν¨ν΄ νμ΅κ³Όμ
μ 4 μ₯ μ€ν
4.1 λ°μ΄ν°μ
κ³Ό ꡬν μΈλΆμ¬ν
4.2 μ±λ₯ λΉκ΅
μ 5 μ₯ κ²°λ‘
ABSTRACTMaste
Timing of Admission to the Surgical Intensive Care Unit is Associated with in-Hospital Mortality
Purpose
The relationship between the timing of admission (work-hours or after-hours) to the intensive care unit (ICU) and mortality among surgical ICU (SICU) patients is unclear. This study aimed to investigate whether admission to SICU during after-hours was associated with in-hospital mortality.
Methods
This retrospective cohort study was conducted in a tertiary academic hospital. The data of 571 patients who were admitted to the SICU and whose complete medical records were available were analyzed. Work-hours were defined as 07:00 to 19:00 Monday to Friday, during which the ICU was staffed with intensivists. After-hours were defined as any other time during which the SICU was not staffed with intensivists. The primary outcome measure was in-hospital mortality according to the time of admission (work-hours or after-hours) to the SICU.
Results
A total of 333 patients, were admitted to the SICU during work-hours, and 238 patients after-hours. Unplanned admissions (47.1% vs. 33.3%, p < 0.001), acute physiology and chronic health evaluation II score β₯ 25 (23.9% vs. 11.1%, p < 0.001), the need for ventilator support (34.0% vs. 17.4%, p < 0.001), and the use of vasopressors (50.0% vs. 33.3%, p < 0.001) were significantly higher in the after-hours group compared with the work-hours group. Multivariate analyses revealed that the timing of SICU admission was an independent predictor of in-hospital mortality (odds ratio, 2.526; 95% confidence interval, 1.010-6.320; p = 0.048).
Conclusion
This study showed that admission to the SICU during after-hours was associated with increased in-hospital mortality.ope
Coexistence of chronic lymphocytic thyroiditis with papillary thyroid carcinoma: clinical manifestation and prognostic outcome
The study aimed to identify the clinical characteristics of coexisting chronic lymphocytic thyroiditis (CLT) in papillary thyroid carcinoma (PTC) and to evaluate the influence on prognosis. A total of 1,357 patients who underwent thyroid surgery for PTC were included. The clinicopathological characteristics were identified. Patients who underwent total thyroidectomy (n = 597) were studied to evaluate the influence of coexistent CLT on prognosis. Among the total 1,357 patients, 359 (26.5%) had coexistent CLT. In the CLT group, the prevalence of females was higher than in the control group without CLT (P < 0.001). Mean tumor size and mean age in the patients with CLT were smaller than without CLT (P = 0.040, P = 0.047, respectively). Extrathyroidal extension in the patients with CLT was significantly lower than without CLT (P = 0.016). Among the subset of 597 patients, disease-free survival rate in the patients with CLT was significantly higher than without CLT (P = 0.042). However, the multivariate analysis did not reveal a negative association between CLT coexistence and recurrence. Patients with CLT display a greater female preponderance, smaller size, younger and lower extrathyroidal extension. CLT is not a significant independent negative predictive factor for recurrence, although presence of CLT indicates a reduced risk of recurrence.ope
Exploring science learning using smartphones in science museums: Focused on the feature of scaffolding
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : κ³Όνκ΅μ‘κ³Ό(μ§κ΅¬κ³Όνμ 곡), 2013. 2. κΉμ°¬μ’
.λνμ μΈ λΉνμ νμ΅ κΈ°κ΄μΈ κ³Όνλ°λ¬Όκ΄μμλ μ μλ¬Όμ λν κ΄λκ°μ μ΄ν΄λ₯Ό λκΈ° μν΄ λΌλ²¨, νλμ§, λμ¨νΈ, ν΄λ μλ΄κΈ°κΈ° λ±μ λ€μν 보쑰 κ΄λ μλ¨μ μ 곡νκ³ μλ€. κ·Έ μ€μμλ μ€λμ€ κ°μ΄λ, PDA, μ€λ§νΈν° λ±μ ν΄λ μλ΄κΈ°κΈ°λ μ΅κ·Ό λ€μ΄ λ§μ κ΄μ¬μ λ°κ³ μλ€. μ°κ΅¬μ λ°λ₯΄λ©΄ ν΄λ μλ΄κΈ°κΈ°λ₯Ό μ΄μ©ν κ΄λμ κΉμ΄ μκ³ λ€μν μ’
λ₯μ ν΄μμ μ 곡ν¨μΌλ‘μ¨ κ΄λκ°λ€μ΄ λ λ§μ νμ΅ κ²½νμ νκ³ , λ κΉμ μ°¨μμ μ΄ν΄μ λΉνμ μ¬κ³ λ₯Ό νκ² ν΄μ€λ€. λν κ΄λκ° μμ μ λ°°κ²½κ³Ό λ λ§μ μ°κ²° κ³ λ¦¬λ₯Ό λ§λ€μ΄ μ£Όμ΄, κ°μΈμ νμ΅(personal learning)μ μ΄μ§μν¬ μ μλ€. λ³Έ μ°κ΅¬μμλ μ΄λ¬ν ν΄λ μλ΄κΈ°κΈ° μ€ μ΅κ·Ό μ£Όλͺ©λ°κ³ μλ μ€λ§νΈν°μ μ΄μ μ λ§μΆμ΄ μ΄γμ€λ±νμλ€μ λμμΌλ‘ μ€λ§νΈν°μ μ΄μ©ν κ³Όνλ°λ¬Όκ΄μμμ κ³Όν νμ΅μ λΆμνμλ€.β
. μλ‘
1. μ°κ΅¬μ νμμ± λ° λͺ©μ
2. μ°κ΅¬ λ¬Έμ
β
‘. μ΄λ‘ μ λ°°κ²½
1. λΉνμ κ³Όννμ΅
1) λΉνμ νμ΅κ³Ό λΉνμ κ³Όννμ΅
2) λ°λ¬Όκ΄μμμ λΉνμ κ³Όννμ΅
2. λΉνμ κ³Όνκ΅μ‘μ λν μ¬νλ¬Ένμ μ κ·Ό
1) λΉνμ κ³Όνκ΅μ‘ μ°κ΅¬μμμ μ¬νλ¬Ένμ μ κ·Όμ νμμ±
2) λΉκ³ μΈ ν€μ νμ΅μ λν κ΄μ κ³Ό μ€μΊν΄λ©
3. κ³Όνλ°λ¬Όκ΄μμ μ€μΊν΄λ© 맀체λ‘μ¨μ μ€λ§νΈν°
1) λͺ¨λ°μΌ κΈ°κΈ°λ₯Ό μ΄μ©ν κ³Όνκ΄μμμ νμ΅
2) μ€λ§νΈν°μ μ΄μ©ν κ³Όνκ΄μμμ νμ΅
β
’. μ°κ΅¬λ°©λ²
1. μ 체 μ°κ΅¬κ³Όμ
2. μ€λ¬Έ μ‘°μ¬
3. νμ₯μ°κ΅¬
β
£. μ°κ΅¬ κ²°κ³Ό
1. κ΄λκ°λ€μ΄ κΈ°λνλ κ³Όνλ°λ¬Όκ΄μμ μ€λ§νΈν°μ μ€μΊν΄λ© μμ±
2. κ·Έλ£Ήμ νΉμ±μ λ°λΌ λνλλ μ€λ§νΈν°μ μ€μΊν΄λ© μμ±
3. κ°μΈμ νΉμ±μ λ°λΌ λνλλ μ€μΊν΄λ©
β
€. κ²°λ‘ λ° μ μΈ
1. κ²°λ‘
2. μ μΈ
μ°Έκ³ λ¬Έν
λΆλ‘Maste
λλ₯Ό ν볡νκ² λ§λλ μ¬λμ λꡬμΈκ°
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μ¬λ¦¬νκ³Ό, 2013. 2. μ΅μΈμ² .μ¬νμ μμμμμ μ κ·Ό λͺ©ν(approach goal)μ ννΌ λͺ©ν(avoidance goal)λ ν볡μ μ€μν μν₯μ λ―ΈμΉλ€. λ°λμ§ν κ²°κ³Όλ₯Ό μΆκ΅¬νλ κ²μ μ΄μ μ λλ μ κ·Ό λͺ©νλ ν볡μ κΈμ μ μΈ μν₯μ λ―ΈμΉμ§λ§, λΆμ μ μΈ κ²°κ³Όλ₯Ό νΌνλ κ²μ μ΄μ μ λλ ννΌ λͺ©νλ ν볡μ λΆμ μ μΈ μν₯μ μ€λ€(Elliot et al., 2006). μ΄λ¬ν λͺ©νμ μ€μν νΉμ§ μ€ νλλ μ€μν νμΈ(significant others)μ μν΄μ μ λ°λ μ μλ€λ κ²μ΄λ€. κ·Έλ¬λ κΈ°μ‘΄ μ°κ΅¬μμλ μ κ·Ό λͺ©νμ ννΌ λͺ©νμ μ¬νμ κ²°κ³Ό(social outcome)μ κ΄κ³λ νλ°ν λ€λ€μμ§λ§ νμΈμ μν΄ μ λλλ μ κ·Ό λͺ©νμ ννΌ λͺ©ν, ν볡μ κ΄κ³μ λν΄ κ΄μ¬μ λ³΄μΈ μ°κ΅¬λ κ±°μ μμλ€. λ³Έ μ°κ΅¬λ μ¬λλ€μ ν볡νκ² λ§λλ νμΈμ μ κ·Ό λͺ©νλ₯Ό μ λνλ μ¬λμ΄κ³ , λμκ° ν볡ν κ΄κ³λ μ κ·Ό λͺ©νκ° μ λλλ κ΄κ³μμ λ°νκ³ μ νλ€. μ΄λ₯Ό μν΄ μΈ κ°μ§ μ°κ΅¬λ₯Ό μνν κ²°κ³Ό, μ¬λλ€μ μμ μ ν볡νκ² λ§λλ μ¬λμ λ μ¬λ¦΄ λ μ κ·Ό λͺ©νλ₯Ό ννΌ λͺ©νλ³΄λ€ λ λ§μ΄ λ μ¬λ ΈμΌλ©°(μ°κ΅¬ 1), μμ μ λ ν볡νκ² λ§λλ μ¬λμ λ μ¬λ¦΄ λ μλμ μΌλ‘ λ ν볡νκ² λ§λλ μ¬λμ λ μ¬λ¦΄ λλ³΄λ€ μ κ·Ό λͺ©νλ₯Ό λ λ§μ΄ λ μ¬λ Έλ€(μ°κ΅¬ 2). λν μ κ·Ό λͺ©νλ₯Ό ννΌ λͺ©νλ³΄λ€ λ λ§μ΄ λ μ¬λ¦΄μλ‘ κ΄κ³ λ§μ‘±λκ° λ λμκ³ , μ΄λ 7κ°μ νμ κ΄κ³ λ§μ‘±λμλ μν₯μ μ£Όμλ€(μ°κ΅¬ 3). λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ μ¬λλ€μ ν볡νκ² νλ νμΈμ μ κ·Ό λͺ©νλ₯Ό μ λνλ μ¬λμμ νμΈν μ μμ λΏλ§ μλλΌ, ν볡ν κ΄κ³μμ μ¬λλ€μ μ κ·Ό λͺ©νλ₯Ό λ λ§μ΄ λ μ¬λ¦°λ€λ κ²μ νμΈν μ μμλ€.μ λ‘ 1
μ°κ΅¬ 1 12
λ°©λ² 13
κ²°κ³Ό λ° λ
Όμ 16
μ°κ΅¬ 2 21
λ°©λ² 22
κ²°κ³Ό λ° λ
Όμ 24
μ°κ΅¬ 3 27
λ°©λ² 28
κ²°κ³Ό λ° λ
Όμ 30
μ’
ν© λ
Όμ 35
μ°Έκ³ λ¬Έν 43
λΆλ‘ 49
Abstract 52Maste
Aesthetics of the Grotesque and Its Ontological Meanings
When we confront grotesque objects, we are struck with unpleasant
feelings, and then we generally find out their essential feature is integration
of disparate things. Overwhelmed with the feelings and unintelligibility, we
cannot represent them as anything. Consequently, they are outside our
conceptual understanding: we always fail when we attempt to reduce them
to something familar to our way of understanding.
It is because our cognitive operation to represent them is nothing more
than forcible operation to keep them within territories of our
understanding. Therefore, there remain only two ways to manage to think
the grotesque: one is to conceive them as ridiculous examples of
sub-culture, and the other is to consider them as exceptions to history of
human thoughts.
However, it is obvious that we cannot explain why aesthetics of the
grotesque have been maintained and developed throughout the whole
passage of art history. Why has the grotesque continued despite of that
kind of ceaseless aesthetic persecution? This is the primary question of this
paper and I want to show what significance we can draw from aesthetics
of the grotesque.
In this paper, I attempt to establish the ontology on which aesthetic
phenomenon called the grotesque can stand. In Gilles Deleuze's
speculations on aesthetics, we can find out impersonal anonymity is playing
key role in the thinker's articulation of aesthetic. I take this concept into
my conceptualization of the grotesque because the concept is similar to
dogmas of aesthetics of the grotesque. From his own theory about
aesthetics, Deleuze constructed philosophy of difference, which provides the
theoretical ground for his unique criticism on legacy of traditional Western
metaphysics. With understanding about Deleuze's metaphysical discussion,
I can contrive ontologically systematic analysis of the grotesque. Along the
development of my discussion, aesthetics of the grotesque is established in
the context of history of thought for the first time.
To achieve this goal, I review historical evaluations of the grotesque, and
then, explain the status occupied by the grotesque in system of human
understanding. Through the passage of this kind of examination, I avoid
rushing into clear definition of the grotesque, but I choose to analyze the
sentiment of unpleasantness delivered to human receptivity at the sight of
grotesque objects. In this way, I can fairly reveal aesthetic meanings of the
grotesque because the effects of the grotesque can keep their possibilities