• Journal of Korea Association of Health Promotion
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• v.2 no.1
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• pp.27-37
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• 2004

Background 'For many years, the Papanicolaou smear has been used to detect pre-malignant and malignant disease of the cervix. Although cervical cytology screening programmes have result in the reduction of cervical cancer incidence and mortality, Pap smear have been subjected to intense scrutiny and criticism in recent years. So cervicography is introduced. Cervicography is an adjunct method of cervical cancer screening intended to complement Papanicolaou smear. Cervicography involve obtaining and evaluating a photographic image of the cervix. The purpose of this investigation was to evaluate the efficacy of Papanicolaou smear and cervicography in cervical cancer screening. Materials & Methods : This study population was of 74 women, who visited department of obstetrics & Gynecology, Korea association of Health Promotion Chung-nam branch from January, 20O2 to October, 2003. All patients were taken Pap smear before cervicography, and then two cervicography was obtained with applying5% acetic acid. Those women in whom abnormalities were detected by either test subsequently obtained histologic specimen. Results : 1. The sensitivity and the specificity of Papanicolaou smear was 92.1% and 72.7%respectively.2. The sensitivity and the specificity of cervicography was 88.9% and 54.5% respectively. The false negative rate, and false positive rate of Papanicolaou smear were 7.9%, 27.2% respectively. The false negative rate, and false positive rate of cervicography were 11.1%,45.5% respectively. Conclusions . Papanicolaou smear is a useful method and an important tool for detecting cervical cancer. However when Papanicolaou smear and Cervicograpy is used together, the sensitivity is higher than for Papanicolaou smear used alone.

• Imaging Science in Dentistry
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• v.48 no.2
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• pp.121-125
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• 2018

Purpose: Detecting laryngeal cartilages (triticeous and thyroid cartilages) on panoramic radiographs is important because they may be confused with carotid artery calcifications in the bifurcation region, which are a risk factor for stroke. This study assessed the efficiency of panoramic radiography in the diagnosis of calcified laryngeal cartilages using cone-beam computed tomography (CBCT) as the reference standard. Materials and Methods: A total of 312 regions(142 bilateral, 10 left, 18 right) in 170 patients(140 males, 30 females) were examined. Panoramic radiographs were examined by an oral and maxillofacial radiologist with 11 years of experience. CBCT scans were reviewed by 2 other oral and maxillofacial radiologists. The kappa coefficient(${\kappa}$) was calculated to determine the level of intra-observer agreement and to determine the level of agreement between the 2 methods. Diagnostic indicators(sensitivity, specificity, accuracy, and false positive and false negative rates) were also calculated. P values <.05 were considered to indicate statistical significance. Results: Eighty-two images were re-examined to determine the intra-observer agreement level, and the kappa coefficient was calculated as 0.709 (P<.05). Statistically significant and acceptable agreement was found between the panoramic and CBCT images (${\kappa}=0.684$ and P<.05). The sensitivity, specificity, diagnostic accuracy rate, the false positive rate, and the false negative rate of the panoramic radiographs were 85.4%, 83.5%, 84.6%, 16.5%, and 14.6%, respectively. Conclusion: In most cases, calcified laryngeal cartilages could be diagnosed on panoramic radiographs. However, due to variation in the calcifications, diagnosis may be difficult.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.3
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.891-899
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• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• Journal of Yeungnam Medical Science
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• v.20 no.2
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• pp.117-128
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• 2003

Thallium-201 scintigraphy is used to discriminate the malignant bone tumor from the benign by qualitatively and quantitatively, and to predict the response of preoperative chemotherapy in osteosarcoma, by comparing the changes of thallium uptake ratio after chemotherapy to the tumor necrosis ratio. Thallium-201 scintigraphy scan should be done prior to surgical biopsy. PICKER Prism 2000 gamma camera with high resolution parallel hole collimator is usually used for scanning. The patient is injected with 2-3mCi of Tl-201 and the early phase is checked in 30 minutes and delayed phase in 3 hours. The scan images are visually evaluated by a blinded nuclear medicine physician. We could evaluate true positive, true negative, false positive and false negative by the comparison of results with those of biopsy, and calculate positive and negative predictive value(%), sensitivity(%), specificity(%) and diagnostic accuracy(%). For the quantitative analysis of thallium uptake, we drew the region of interest on the tumor side and contralateral normal side as mirror image, and calculated the uptake ratio with dividing the amount of gamma count in tumor side by normal side. We could calculate the percent changes of thallium uptake ratio in early and delayed phase, and compare them to the ratio of tumor necrosis. Thallium-201 scintigraphy proved as useful imaging study to discriminate malignant bone tumor from benign, but had exception in giant cell tumor and low grade malignant bone tumors. We can use T1-201 scan to differentiate the benign from the malignant tumor, and to evaluate the response of preoperative chemotherapy or radiotherapy, and to determine the residual tumor or local recurrence. For the better result, we need to have a more detail information about false positive cases and a more objective and quantitative reading technique.

• Korean Journal of Otorhinolaryngology-Head and Neck Surgery
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• v.60 no.3
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• pp.107-111
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• 2017

Background and Objectives The bedside head impulse test (bHIT) in bare eyes often overlooks possible vestibular losses by missing the corrective saccade. This is why it is necessary to compare bHIT against video head impulse test (vHIT), which is more accurate in identifying vestibular losses than the bedside test. Subjects and Method A total of 51 vHIT positive ears underwent the study, and out of those, 47 were diagnosed with dizziness. bHIT and vHIT were performed for patients, and the occurrence rate of overt saccade (OS) was calculated. Results Among the 51 vHIT positive ears, 33 (64.7%) were bHIT positive ears and 18 ears (35.3%) were bHIT negative. Patterns of positive vHIT were classified as A: no corrective saccade, B: covert saccade (CS) only, C: OS only, and D: CS with OS (CS+OS), which were 45 out of 51 ears (88%). The occurrence rate of OS was higher in the bHIT positive group than in the bHIT negative group (p=0.05), and higher in the CS negative group (CS-) than in the CS positive group (CS+) (p<0.001). Conclusion Possible causes of false negative results of bHIT are seen as following: the absence of corrective (covert and overt) saccade, the occurrence of CS only, and missing the OS during the bHIT (probably due to low occurrence rate of OS). The occurrence of CS should be considered as an important factor in false negative bHIT when lowering the occurrence rate of OS.

• Journal of the Korean Society of Radiology
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• v.84 no.2
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• pp.361-371
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• 2023

The success of image-guided breast biopsy depends on the biopsy method, needle selection, and appropriate technique based on the accurate judgment by the radiologist at biopsy. However, insufficient or inappropriate sampling of specimens may result in false-negative results or pathologic underestimation. Therefore, image-pathology concordance assessments after biopsy are essential for appropriate patient management. Particularly, the assessment of image-pathology concordance can avoid false-negative reports of breast cancer as a benign pathology. Therefore, this study aimed to discuss factors that impact the accurate interpretation of image-guided breast biopsy along with the appropriate assessments.

• Journal of Microbiology and Biotechnology
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• v.16 no.11
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• pp.1773-1777
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• 2006

We evaluated the usefulness of DNA microarray as a comparative genomics tool, and tested the validity of the cutoff values for defining absent genes in test genomes. Three genome-sequenced E. coli strains (K-12, EDL933, and CFT073) were subjected to comparative genomic hybridization with DNA microarrays covering almost all ORFs of the reference strain K-12, and the microarray results were compared with the results obtained from in silico analyses of genome sequences. For defining the K-12 ORFs absent in test genomes (reference strain-specific ORFs), we applied and evaluated the cutoff level of -1. The average sequence similarity between ORFs, to which corresponding spots showed a log-ratio of>-1, was $96.9{\pm}4.8$. The numbers of spots showing a log-ratio of <-1 (P<0.05, t-test) were 90 (2.5%) and 417 (10.6%) for the EDL933 genome and the CFT073 genome, respectively. Frequency of false negatives (FN) was ca. 0.2, and the cutoff level of -1.3 was required to achieve the FN of 0.1. The average sequence similarity of the false negative ORFs was $77.8{\pm}14.8$, indicating that the majority of the false negatives were caused by highly divergent genes. We concluded that the microarray is useful for identifying missing or divergent ORFs in closely related prokaryotic genomes.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.26-32
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• 2014

Purpose: As PET-MRI which has excellent soft tissue contrast is developed as integration system, many researches about clinical application are being conducted by comparing with existing display equipments. Because PET-MRI is actively used for head and neck cancer diagnosis in our hospital, lymph node metastasis before the patient's surgery was diagnosed and clinical usefulness of head and neck cancer PET-MRI scan was evaluated using pathological opinions and idiopathy surrounding tissue metastasis evaluation method. Materials and Methods: Targeting 100 head and neck cancer patients in SNUH from January to August in 2013. $^{18}F-FDG$ (5.18 MBq/kg) was intravenous injected and after 60 min of rest, torso (body TIM coil, Vibe-Dixon) and dedication (head-neck TIM coil, UTE, Dotarem injection) scans were conducted using $Bio-graph^{TM}$ mMR 3T (SIEMENS, Munich). Data were reorganized using iterative reconstruction and lymph node metastasis was read with Syngo.Via workstation. Subsequently, pathological observations and diagnosis before-and-after surgery were examined with integrated medical information system (EMR, best-care) in SNUH. Patient's diagnostic information was entered in each category of $2{\times}2$ decision matrix and was classified into true positive (TP), true negative (TN), false positive (FP) and false negative (FN). Based on these classified test results, sensitivity, specificity, accuracy, false negative and false positive rate were calculated. Results: In PET-MRI scan results of head and neck cancer patients, positive and negative cases of lymph node metastasis were 49 and 51 cases respectively and positive and negative lymph node metastasis through before-and-after surgery pathological results were 46 and 54 cases respectively. In both tests, TP which received positive lymph node metastasis were analyzed as 34 cases, FP which received positive lymph node metastasis in PET-MRI scan but received negative lymph node metastasis in pathological test were 4 cases, FN which received negative lymph node metastasis but received positive lymph node metastasis in pathological test was 1 case, and TN which received negative lymph node metastasis in both two tests were 50 cases. Based on these data, sensitivity in PET-MRI scan of head and neck cancer patient was identified to be 97.8%, specificity was 92.5%, accuracy was 95%, FN rate was 2.1% and FP rate was 7.00% respectively. Conclusion: PET-MRI which can apply the acquired functional information using high tissue contrast and various sequences was considered to be useful in determining the weapons before-and-after surgery in head and neck cancer diagnosis or in the evaluation of recurrence and remote detection of metastasis and uncertain idiopathy cervical lymph node metastasis. Additionally, clinical usefulness of PET-MRI through pathological test and integrated diagnosis and follow-up scan was considered to be sufficient as a standard diagnosis scan of head and neck cancer, and additional researches about the development of optimum MR sequence and clinical application are required.

• Journal of Korean Neurosurgical Society
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• v.66 no.1
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.