Comparison of Ocular Biometry and Refractive Outcomes Using IOL Master 700, IOL Master 500, and Ultrasound

PURPOSE: To compare the new swept-source optical coherence tomography based IOL Master 700 to both the partial coherence interferometry based IOL Master 500 and ultrasound A-scan in terms of the ocular biometry and the prediction of postoperative refractive outcomes. METHODS: A total 67 eyes of 55 patients who received cataract surgery were included in our study. The axial length, anterior chamber depth, and keratometry were measured using IOL Master 700, IOL Master 500, and A-scan. The predictive errors, which are the differences between predictive refraction and post-operative refraction 1 month after surgery, were also compared. RESULTS: Axial length measurements were not successful in 5 eyes measured using IOL Master 700 and in 12 eyes measured using IOL Master 500. The mean absolute postoperative refraction predictive errors were 0.63 ± 0.50 diopters, 0.66 ± 0.51 diopters, and 0.62 ± 0.51 diopters for IOL Master 700, IOL Master 500, and A-scan, respectively, and these values exhibited no statistically significant differences. The mean axial lengths were 24.25 ± 2.41 mm, 24.24 ± 2.40 mm, and 24.22 ± 2.39 mm; the mean anterior chamber depths were 3.09 ± 0.39 mm, 3.17 ± 0.39 mm, and 3.15 ± 0.46 mm; and the mean keratometry values were 44.12 ± 1.82 diopters, 44.57 ± 2.10 diopters, and 43.98 ± 1.84 diopters for the IOL Master 700, IOL Master 500, and A-scan groups, respectively. None of these parameters showed statistically significant differences between the three groups. Regarding pair-wise comparison, there were significant differences between the IOL Master 700 and the other devices. CONCLUSIONS: The ocular biometric measurements measured using IOL Master 700, IOL Master 500, and A-scan showed no significant differences. However, IOL Master 700 demonstrated a superior ability to successfully take biometric measurements compared to IOL Master 500. Therefore, IOL Master 700 is capable of measuring ocular biometry for cataract surgery in clinical practice.

Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

PURPOSE: A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. MATERIALS AND METHODS: We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). RESULTS: SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. CONCLUSION: Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

Ureteroscopy under Intravenous Propofol Anesthesia for the Treatment of Middle and Upper Ureteral Calculi: Comparison with Lower Ureteral Calculi / 대한비뇨기과학회지

PURPOSE: Intravenous propofol anesthesia has been safely and effectively used in the ureteroscopic management of lower ureteral calculi. We investigated whether we can also use propofol anesthesia effectively for the treatment of middle and upper ureteral calculi. MATERIALS AND METHODS: We performed ureteroscopy with intravenous propofol anesthesia in 200 consecutive patients with ureteral calculi from July 1998 to December 2001. We divided them into 2 groups. Group 1 consisted of 59 patients, 48 men and 11 women, with upper and midureteral calculi, and group 2 consisted of 141 patients, 94 men and 47 women, with lower ureteral calculi. We compared safety, success rate, length of hospital stay and complication rate between the two groups. RESULTS: The overall success rate for ureteroscopy was 94.5% (189/200). Seven patients suffered perioperative complications, none of whom needed to change mode of anesthesia or remembered discomfort during the operation. In group 1, stone size, operation time and postoperative hospital stays were 7.5+/-3.2mm, 33.0+/-20.3 minutes and 1.9+/-1.0 days, respectively. The equivalent results were 6.2+/-2.9mm, 22.9+/-15.0 minutes and 1.4+/-0.9 days, respectively, in group 2. There were no significant differences in age, complication rate or success rate between the two groups. CONCLUSIONS: In patients with middle and upper ureteral calculi, ureteroscopic stone removal under intravenous propofol anesthesia can be performed safely and effectively without any significant increase in morbidity or patient discomfort.