• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.237-240
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• 2002

Knowing the dose distribution in a tissue is as important as being able to measure exposure or absorbed dose in radiotherapy. Since the Dry Imager spread, the wet type automatic processor is no longer used. Furthermore, the waste fluid after film development process brings about a serious problem for prevention of pollution. Therefore, we have developed a measurement method for the dose distribution (CR dosimetry) in the phantom based on the imaging plate (IP) of the computed radiography (CR). The IP was applied for the dose measurement as a dosimeter instead of the film used for film dosimetry. The data from the irradiated IP were processed by a personal computer with 10 bits and were depicted as absorbed dose distributions in the phantom. The image of the dose distribution was obtained from the CR system using the DICOM form. The CR dosimetry is an application of CR system currently employed in medical examinations to dosimetry in radiotherapy. A dose distribution can be easily shown by the Dose Distribution Depiction System we developed this time. Moreover, the measurement method is simpler and a result is obtained more quickly compared with film dosimetry.

• Journal of The Korean Society of Integrative Medicine
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• v.7 no.4
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• pp.265-272
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• 2019

Purpose : Excessive computer use frequently results in musculoskeletal disorders of the neck and shoulder such as forward head posture (FHP). The purpose of this study was to investigate effects of neck and shoulder exercise program on spino-pelvic alignment and the correlation between change in head and neck posture and spino-pelvic alignment in FHP. Methods : The study included 44 participants with FHP. The participants performed the exercise for correction of FHP 2-3 times a week for 4 weeks. We examined whole spine X-ray images in the lateral standing position with both arms crossed. We measured anterior head translation distance (AHT), craniovertebral angle (CVA), cervical lordosis (CL), thoracic kyphosis (TK), lumbosacral lordosis (LSL), sacral slope (SS), pelvic tilt (PT), and pelvic incidence (PI) of the subjects. The association between change in AHT and each spino-pelvic parameter was also subjected to Pearson's correlation coefficient analysis. Results : There were statistically significant differences before and after exercise in the parameters of AHT, CVA, and SS (p<.05). Significant negative correlation was observed between the change in AHT and CVA (r=-.768, p<.001), and CL (r=-.388, p<.05). There was significant positive correlation between the change in AHT and SS (r=.328, p<.05), and PI (r=.333, p<.05). However, no significant correlation was observed in change in AHT with that of TK, LSL, and PT. Conclusion : Based on the above results, we conclude that there is a relationship between change in AHT, which is a parameter associated with forward displacement of the head, and that of CVA, CL, SS, and PI after exercise in cases of FHP.

• Journal of Veterinary Clinics
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• v.28 no.3
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• pp.303-306
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• 2011

A 12-year-old female mixed breed dog receiving a progesterone drug was referred for evaluation of an abdominal mass. Abdominal radiography and ultrasonography revealed a swollen uterus and an associated mass. Serum chemistry revealed hyperglobulinemia consistent with acute inflammation based on the results of serum protein electrophoresis. Fine needle aspiration of the mass guided by ultrasonography was performed for cytological evaluation. The cytological impression was consistent with adenocarcinoma. Exploratory laparotomy identified a uterine body mass, which was surgically removed for histopathology. Histology of the mass identified a uterine adenocarcinoma. Immunochemistry using anti-estrogen and progesterone receptor antibodies was performed and neoplastic cells were negative to both antibodies while some normal elements were reactive to both of them. Computer tomography demonstrated evidence of metastatic disease in the lung one week after the surgery and the dog died about 40 days after surgery.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.187-193
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• 2021

Purpose: This study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification. Materials and Methods: Two hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process. Results: Approximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients. Conclusion: Personal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.

• Journal of Periodontal and Implant Science
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• v.51 no.4
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• pp.285-295
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• 2021

Purpose: The aims of the present study were 1) to quantitatively evaluate the extent of sinus pneumatization and 2) to determine the factors affecting sinus pneumatization. Methods: Based on implant treatment records, a list of patients who underwent implant placement on the posterior maxilla was obtained. Among them, patients with pre-extraction and post-extraction (before implant placement) panoramic radiographs were selected. After excluding radiographs with low resolution and image distortion, the radiographs before and after extraction were superimposed using computer software. Subsequently, the extent of sinus pneumatization (the vertical change of the sinus floor) was measured. Simple and multiple mixed models were used to determine the factors affecting sinus pneumatization. Results: A total of 145 patients were eligible for the present investigation. The average extent of sinus pneumatization was 1.56±3.93 mm at 176 tooth sites. Male sex, single tooth extraction, extraction of an endodontically compromised tooth, a class I root-sinus relationship, and sinus membrane thickening >10 mm favored pneumatization, but without statistical significance. The maxillary second molar presented the greatest pneumatization (2.25±4.39 mm) compared with other tooth types. This finding was confirmed in the multiple mixed model, which demonstrated a statistically significant impact of the extraction of a second molar compared with the extraction of a first premolar. Conclusions: Maxillary sinus pneumatization was 1.56±3.93 mm on average. The extraction of a second molar led to the greatest extent of pneumatization, which should be considered in the treatment plan for this tooth site.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Journal of Periodontal and Implant Science
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• v.54 no.1
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

• Korean Journal of Radiology
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• v.23 no.1
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• pp.139-149
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• 2022

Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

• Korean Journal of Radiology
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• v.24 no.3
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• pp.259-270
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• 2023

Objective: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. Materials and Methods: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit. Results: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. Conclusion: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.

• Progress in Medical Physics
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• v.18 no.1
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• pp.27-34
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• 2007

Respiration sating radiotherapy technique developed In consideration of the movement of body surface and Internal organs during respiration, is categorized into the method of analyzing the respiratory volume for data processing and that of keeping track of fiducial landmark or dermatologic markers based on radiography. However, since these methods require high-priced equipments for treatment and are used for the specific radiotherapy. Therefore, we should develop new essential method whilst ruling out the possible problems. This study alms to obtain body surface motion by using the couch based computer-controlled motion phantom (CBMP) and US sensor, and to develop respiration gating techniques that can adjust patients' beds by using opposite values of the data obtained. The CBMP made to measure body surface motion is composed of a BS II microprocessor, sensor, host computer and stopping motor etc. And the program to control and operate It was developed. After the CBMP was adjusted by entering random movement data, and the phantom movements were acquired using the sensors, the two data were compared and analyzed. And then, after the movements by respiration were acquired by using a rabbit, the real-time respiration gating techniques were drawn by operating the phantom with the opposite values of the data. The result of analysing the acquisition-correction delay time for the data value shows that the data value coincided within 1% and that the acquistition-correction delay time was obtained real-time $(2.34{\times}10^{-4}sec)$. And the movement was the maximum movement was 6 mm In Z direction, In which the respiratory cycle was 2.9 seconds. This study successfully confirms the clinical application possibility of respiration gating techniques by using a CBWP and sensor.