Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance. Objective: The primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience. Methods: This simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists. Results: With AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P<.001 in the t test. With AI guidance, the average score (accuracy) of the junior group increased to 46.581 (52%), reaching a level similar to that of the senior embryologists of 44.833 (50%), with P=.34. Junior embryologists had a higher level of trust in the AI score. Conclusions: This study demonstrates the potential benefits of AI in selecting embryos with high chances of pregnancy, particularly for embryologists with 5 years or less of experience, possibly due to their trust in AI. Thus, using AI as an auxiliary tool in clinical practice has the potential to improve embryo assessment and increase the probability of a successful pregnancy.

Background: Renal cell carcinoma (RCC) has a high recurrence rate of 20% to 30% after nephrectomy for clinically localized disease, and more than 40% of patients eventually die of the disease, making regular monitoring and constant management of utmost importance. Objective: The objective of this study was to develop an algorithm that predicts the probability of recurrence of RCC within 5 and 10 years of surgery. Methods: Data from 6849 Korean patients with RCC were collected from eight tertiary care hospitals listed in the KOrean Renal Cell Carcinoma (KORCC) web-based database. To predict RCC recurrence, analytical data from 2814 patients were extracted from the database. Eight machine learning algorithms were used to predict the probability of RCC recurrence, and the results were compared. Results: Within 5 years of surgery, the highest area under the receiver operating characteristic curve (AUROC) was obtained from the naïve Bayes (NB) model, with a value of 0.836. Within 10 years of surgery, the highest AUROC was obtained from the NB model, with a value of 0.784. Conclusions: An algorithm was developed that predicts the probability of RCC recurrence within 5 and 10 years using the KORCC database, a large-scale RCC cohort in Korea. It is expected that the developed algorithm will help clinicians manage prognosis and establish customized treatment strategies for patients with RCC after surgery.

Patients with brain metastasis (BM) have a better prognosis when it is detected early. However, current guidelines recommend brain imaging only when there are central nervous system symptoms or abnormal experimental values. Therefore, metastases are discovered later in asymptomatic patients. As a result, there is a need for an algorithm that predicts the possibility of BM using clinical data and machine learning (ML). Data from 3153 patients with renal cell carcinoma (RCC) were collected from the 11-institution Korean Renal Cancer Study group (KRoCS) database. To predict BM, clinical information of 1282 patients was extracted from the database and used to compare the performance of six ML algorithms. The final model selection was based on the area under the receiver operating characteristic (AUROC) curve. After optimizing the hyperparameters for each model, the adaptive boosting (AdaBoost) model outperformed the others, with an AUROC of 0.716. We developed an algorithm to predict the probability of BM in patients with RCC. Using the developed predictive model, it is possible to avoid detection delays by performing computed tomography scans on potentially asymptomatic patients.

Background: We determined the cost-effectiveness of the anti-vascular endothelial growth factor (VEGF) intravitreal injection versus panretinal photocoagulation (PRP) for patients with proliferative diabetic retinopathy (PDR) in South Korea. Methods: We simulated four treatment strategies using PRP and the anti-VEGF injection by constructing a Markov model for a hypothetical cohort of 50-year-old PDR patients: (1) PRP only; (2) anti-VEGF injection only; (3) PRP first; and (4) anti-VEGF injection first. Results: In this cost-effectiveness analysis, compared with only-PRP, the incremental cost-effectiveness ratio was $95,456 per quality-adjusted life-year (QALY) for PRP first, $34,375 per QALY for anti-VEGF injection first, and $33,405 per QALY for anti-VEGF injection only from a healthcare perspective. From the societal and payer perspective, strategy (2) was more cost-saving and effective than (1). In the probabilistic sensitivity analysis, only-PRP was cost-effective up to the willingness-to-pay (WTP) of about $42,000, while anti-VEGF injection only was cost-effective from a healthcare perspective. From the societal and payer perspectives, regardless of the value of WTP, anti-VEGF injection only was the most cost-effective strategy. Conclusion: In our study, the anti-VEGF injection for PDR was cost-effective from the payer and societal perspectives.

What is known and objectives In Korea, the side effects of sodium‐glucose cotransporter 2 inhibitors (SGLT2i) have not been clearly reported, aside from voluntary reporting. We aimed to develop detection algorithms for SGLT2i‐related genital tract infections (GTIs) and urinary tract infections (UTIs) via a common data model (CDM), an electronic medical record–based database for supporting multi‐hospital clinical research. We estimated the occurrence of GTIs and UTIs and—by assessing the status of each step of the algorithm—we also aimed to determine how clinicians responded to the SGLT2i‐related GTIs and UTIs. Methods We targeted all patients who were prescribed SGLT2i at Catholic University Seoul St. Mary's Hospital and Hallym University Dongtan Sacred Heart Hospital from January 2014 to August 2018. We developed algorithms for detection of SGLT2i‐related GTIs or UTIs that divided patients into "most likely," "possibly" or "less likely" categories of GTIs or UTIs. The numbers of patients at each step were extracted. Results and discussion A total of 4253 patients received their first prescription of SGLT2i. According to the algorithm used in this study, the proportions of "most likely GTI" and "possibly GTI" were 0.9% (37 out of 4253) and 19.4% (826 out of 4253 patients), respectively. Similarly, the proportions of "most likely UTI" and "possibly UTI" were 0.9% (38 out of 4253) and 20.2% (858 out of 4253 patients), respectively. Compared to the various existing prospective studies, both GTIs and UTIs showed lower occurrence among patients who met "most likely" criteria and higher occurrence among those who met "possibly" criteria. When a GTI or UTI occurred or was suspected, the overall rate of discontinuing SGLT2i was 51.8% (1721 out of 3323). Despite a confirmed or suspected GTI and an UTI, 62.8% (1460 out of 2323) and 14.2% (142 out of 1000) of patients continued to take SGLT2i, respectively. The discontinuation rate for suspected GTIs was significantly lower than that for suspected UTIs (37.2% vs. 85.8%, p < 0.001). What is new and conclusion In this study, although the GTIs appeared to have a similar occurrence as UTIs, however, the discontinuation rate of SGLT2i for suspected GTIs was relatively lower. Our study is novel in that we identified how the physicians approached SGLT2i‐related GTIs or UTIs at each step in a real‐world clinical practice setting. Although we could estimate SGLT2i‐related GTIs and UTIs via CDM, we were limited in our ability to accurately detect mild drug side effects via CDM, which lacked data for operational definition.

We developed an mHealth application that can help alleviate the problematic use of smartphones and allied psychological symptoms. This study observed the change in patterns of users’ problematic smartphone use, depression, and anxiety while using the mHealth application. We conducted this study from 9 January to 10 April 2019. The Korean Smartphone Addiction Proneness Scale for Adults, Generalized Anxiety Disorder Scale, and the Patient Health Questionnaire were measured at week 0, 8, 12. A post hoc test of Repeated Measurement Anova analysis and Linear Mixed Model analysis were used. Overall, 190 participants were allocated into two groups. Sixty-six were in the smartphone control-use group and 124 were in the problematic-use group. The study elucidated the difference between the two groups in terms of problematic smartphone use and depression and anxiety after 13 weeks of using the mHealth application. This study showed the use of the mHealth application reducing problematic smartphone use scores and negative symptoms such as anxiety and depression in the PSU group. The results can be used as the basis for similar qualitative studies to further resolve the problematic use of smartphones.