In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery.

In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model's adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity.

Adequacy of prostate cancer prevention and screening recommendations provided by an artificial intelligence-powered large language model.

PURPOSE: We aimed to assess the appropriateness of ChatGPT in providing answers related to prostate cancer (PCa) screening, comparing GPT-3.5 and GPT-4. METHODS: A committee of five reviewers designed 30 questions related to PCa screening, categorized into three difficulty levels. The questions were formulated identically for both GPTs three times, varying the prompts. Each reviewer assigned a score for accuracy, clarity, and conciseness. The readability was assessed by the Flesch Kincaid Grade (FKG) and Flesch Reading Ease (FRE). The mean scores were extracted and compared using the Wilcoxon test. We compared the readability across the three different prompts by ANOVA. RESULTS: In GPT-3.5 the mean score (SD) for accuracy, clarity, and conciseness was 1.5 (0.59), 1.7 (0.45), 1.7 (0.49), respectively for easy questions; 1.3 (0.67), 1.6 (0.69), 1.3 (0.65) for medium; 1.3 (0.62), 1.6 (0.56), 1.4 (0.56) for hard. In GPT-4 was 2.0 (0), 2.0 (0), 2.0 (0.14), respectively for easy questions; 1.7 (0.66), 1.8 (0.61), 1.7 (0.64) for medium; 2.0 (0.24), 1.8 (0.37), 1.9 (0.27) for hard. GPT-4 performed better for all three qualities and difficulty levels than GPT-3.5. The FKG mean for GPT-3.5 and GPT-4 answers were 12.8 (1.75) and 10.8 (1.72), respectively; the FRE for GPT-3.5 and GPT-4 was 37.3 (9.65) and 47.6 (9.88), respectively. The 2nd prompt has achieved better results in terms of clarity (all p < 0.05). CONCLUSIONS: GPT-4 displayed superior accuracy, clarity, conciseness, and readability than GPT-3.5. Though prompts influenced the quality response in both GPTs, their impact was significant only for clarity.

Evaluation of ChatGPT for Patient Counseling in Kidney Stone Clinic: A Prospective Study.

Introduction: The potential of large language models (LLMs) is to improve the clinical workflow and to make patient care efficient. We prospectively evaluated the performance of the LLM ChatGPT as a patient counseling tool in the urology stone clinic and validated the generated responses with those of urologists. Methods: We collected 61 questions from 12 kidney stone patients and prompted those to ChatGPT and a panel of experienced urologists (Level 1). Subsequently, the blinded responses of urologists and ChatGPT were presented to two expert urologists (Level 2) for comparative evaluation on preset domains: accuracy, relevance, empathy, completeness, and practicality. All responses were rated on a Likert scale of 1 to 10 for psychometric response evaluation. The mean difference in the scores given by the urologists (Level 2) was analyzed and interrater reliability (IRR) for the level of agreement in the responses between the urologists (Level 2) was analyzed by Cohen's kappa. Results: The mean differences in average scores between the responses from ChatGPT and urologists showed significant differences in accuracy (p < 0.001), empathy (p < 0.001), completeness (p < 0.001), and practicality (p < 0.001), except for the relevance domain (p = 0.051), with ChatGPT's responses being rated higher. The IRR analysis revealed significant agreement only in the empathy domain [k = 0.163, (0.059-0.266)]. Conclusion: We believe the introduction of ChatGPT in the clinical workflow could further optimize the information provided to patients in a busy stone clinic. In this preliminary study, ChatGPT supplemented the answers provided by the urologists, adding value to the conversation. However, in its current state, it is still not ready to be a direct source of authentic information for patients. We recommend its use as a source to build a comprehensive Frequently Asked Questions bank as a prelude to developing an LLM Chatbot for patient counseling.

Comparison of outcomes after open versus robotic kidney transplantation: A systematic review and meta-analysis.

Introduction: This meta-analysis compares the clinical outcomes of robot-assisted kidney transplant (RAKT) to open kidney transplant (OKT). Methods: A systematic search of Scopus and MEDLINE databases was carried out using a combination of keywords to identify studies comparing RAKT to OKT. Baseline characteristics and preoperative and postoperative data were collected along with data on the short- and long-term outcomes. The study was registered in PROSPERO and Assessing the Methodological Quality of Systematic Reviews and Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were followed. Results: A total of 16 studies were included with a total of 2555 patients, of which 677 underwent RAKT and 1878 underwent OKT. This meta-analysis shows a significant benefit of RAKT over OKT in terms of less intra-operative blood loss, smaller incision length, less postoperative pain scores at 24 and 48 hours, and a lower incidence of surgical site infections (SSIs), especially in obese patients. In addition, the incidence of postoperative lymphoceles was lower in the RAKT group compared to the OKT group, although not statistically significant. There was no difference between the two groups in terms of short-term graft functional outcomes and overall survival. The number of deceased donor recipients undergoing RAKT was very small. At the time of reporting this meta-analysis, no randomized controlled trials (RCTs) had been published. Conclusion: This meta-analysis showed that RAKT is a safe and feasible alternative to OKT, especially in obese individuals. Further trials are needed to confirm the safety, efficacy, and cost-effectiveness of RAKT.

Emergence of artificial generative intelligence and its potential impact on urology.

INTRODUCTION: Artificial generative intelligence (AGI) and large language models (LLMs) have gained significant attention in healthcare and hold enormous promise for transforming every aspect of our life and urology is no exception. MATERIALS AND METHODS: We conducted a comprehensive literature search of electronic databases and included articles discussing AGI and LLMs in healthcare. Additionally, we have incorporated our experiences interacting with the ChatGPT and GPT-4 in different situations with real case reports and case constructs. RESULTS: Our review highlights the potential applications and likely impact of these technologies in urology, for differential diagnosis, prioritizing treatment options, and facilitating research, surgeon, and patient education. At their current developmental stage, we have recognized the need for concurrent validation and continuous human interaction necessary to induce inverse reinforced learning with human feedback to mature them to authenticity. We need to consciously adjust to the hallucinations and guard patients' confidentiality before their extensive implementations in clinical practice. We propose possible remedies for these shortcomings and emphasize the critical role of human interaction in their evolution. CONCLUSION: The integration of these tools has the potential to revolutionize urology, but it also presents several challenges needing attention. To harness the full potential of these models, urologists must consistently engage in training these tools with their clinical sense and experience. We urge the urology community to actively participate in AGI and LLM development to address potential challenges. These models could help us in unleashing our full potential and help us achieve a better work-life balance.

Off-clamp Versus On-clamp Robot-assisted Partial Nephrectomy: A Propensity-matched Analysis.

BACKGROUND: Partial nephrectomy is the preferred treatment option for the management of small renal masses. On-clamp partial nephrectomy is associated with a risk of ischemia and a greater loss of postoperative renal function, while the off-clamp procedure decreases the duration of renal ischemia, leading to better renal function preservation. However, the efficacy of the off- versus on-clamp partial nephrectomy for renal function preservation remains debatable. OBJECTIVE: To compare perioperative and functional outcomes following off- and on-clamp robot-assisted partial nephrectomy (RAPN). DESIGN, SETTING, AND PARTICIPANTS: This study used the prospective multinational collaborative Vattikuti Collective Quality Initiative (VCQI) database for RAPN. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary objective of this study was the comparison of perioperative and functional outcomes between patients who underwent off- and on-clamp RAPN. Propensity scores were calculated for age, sex, body mass index (BMI), renal nephrometry score (RNS) and preoperative estimated glomerular filtration rate (eGFR). RESULTS AND LIMITATIONS: Of the 2114 patients, 210 had undergone off-clamp RAPN and others on-clamp procedure. Propensity matching was possible for 205 patients in a 1:1 ratio. After matching, the two groups were comparable for age, sex, BMI, tumor size, multifocality, tumor side, face of tumor, RNS, polar location of the tumor, surgical access, and preoperative hemoglobin, creatinine, and eGFR. There was no difference between the two groups for intraoperative (4.8% vs 5.3%, p = 0.823) and postoperative (11.2% vs 8.3%, p = 0.318) complications. Need for blood transfusion (2.9% vs 0, p = 0.030) and conversion to radical nephrectomy (10.2% vs 1%, p < 0.001) were significantly higher in the off-clamp group. At the last follow-up, there was no difference between the two groups for creatinine and eGFR. The mean fall in eGFR at the last follow-up compared with that at baseline was equivalent between the two groups (-16.0 vs -17.3 ml/min, p = 0.985). CONCLUSIONS: Off-clamp RAPN does not result in better renal functional preservation. Alternatively, it may be associated with increased rates of conversion to radical nephrectomy and need for blood transfusion. PATIENT SUMMARY: With this multicentric study, we noted that performing robotic partial nephrectomy without clamping the blood supply to the kidney is not associated with better preservation of renal function. However, off-clamp partial nephrectomy is associated with increased rates of conversion to radical nephrectomy and blood transfusion.

Robot-assisted partial nephrectomy in morbidly obese patients: a VCQI database study.

To compare perioperative outcomes following robot-assisted partial nephrectomy (RAPN) in patients with morbid obesity (body mass index (BMI > 40 kg/m2)) and non-obese patients. Using the Vattikuti Collective quality initiative (VCQI) database for RAPN, data for morbidly obese and non-obese patients was obtained. Propensity scores were calculated for two treatment groups (morbidly obese vs. non-obese) for the following variables i.e. age, sex, tumor size, RNS, surgical access (retroperitoneal/transperitoneal) and estimated glomerular filtration rate (eGFR) to ensure comparability. The primary outcome for the study was comparison of trifecta between the two groups. In this study, 158 morbidly obese patients were matched with 158 non-obese patients undergoing RAPN. Two groups matched well for age, sex, tumor size, eGFR and RNS. There was no difference between two groups for ischemia time, blood loss, blood transfusion, conversion to radical nephrectomy, length of stay, intraoperative and postoperative complications. Operative time was longer in morbidly obese patients (median 210 min vs. 120 min, p = 0.000). On pathological analysis, malignant tumors were more likely in the morbidly obese group (83.1% vs.73.4%, p = 0.018). Trifecta outcomes were comparable between the two groups (60.1% vs. 63.3%, p = 0.563). The Median duration of follow-up was 12 months (1-96 months). The morbidly obese group had significantly higher day one creatinine (1.25 ± 0.7 vs. 1.07 ± 0.37, p = 0.001) and significantly lower day one eGFR (62.1 ± 19 vs. 69.2 ± 21, p = 0.018). However, there was no difference between the two groups for the last follow-up creatinine and eGFR. RAPN in morbidly obese patients is associated with equivalent perioperative outcomes compared to non-obese patients.

15-year biochemical failure, metastasis, salvage therapy, and cancer-specific and overall survival rates in men treated with robotic radical prostatectomy for PSA-screen detected prostate cancer.

BACKGROUND: An informed decision regarding a treatment option requires data on its long-term efficacy and side-effect profile. While the side-effects of robotic radical prostatectomy have been well-quantified, the data on its long-term efficacy are lacking. We here provide 15-year oncological outcomes of clinically-localized prostate cancer (CLPCa) patients treated with robot-assisted laparoscopic prostatectomy (RALP). METHODS: We treated 1,807 men with CLPCa with RALP between 2001 and 2005 and prospectively collected follow-up data through 2020. We examined the rates of biochemical failure (BCF), metastatic progression, secondary therapy use, PCa-specific mortality (PCSM), and overall survival (OS) using Kaplan-Meier and competing-risk cumulative incidence methods as appropriate. RESULTS: The median follow-up was 14.1 years. Six hundred eight and 312 men had D'Amico intermediate- and high-risk disease, respectively. Overall, the 15-year rates of BCF, metastasis, secondary therapy use, PCSM, and OS were 28.1%, 4.0%, 16.3%, 2.5%, and 82.1%, respectively. The rates of oncologic failure increased with increasing D'Amico (preoperative) and Diaz (postoperative) risk scores - BCF, metastasis, and PCSM rates in D'Amico low-, intermediate-, and high-risk groups at 15-years were 15.2%, 38.3%, and 44.1% [BCF], 1.1%, 4.1%, and 13.0% [metastasis], and 0.5%, 3.4%, and 6.6% [PCSM], respectively, and in Diaz risk groups 1, 2, 3, 4, and 5 were 5.5%, 20.6%, 41.8%, 66.9%, and 89.2% [BCF], 0%, 0.5%, 3.2%, 20.5%, and 60.0% [metastasis], and 0%, 0.8%, 0.6%, 13.5%, and 37.5% [PCSM], respectively. The OS rates in D'Amico low-to-high and Diaz 1-to-5 risk groups at 15-years were 85.9%, 78.6%, and 75.2%, and 89.4%, 83.2%, 80.6%, 67.2%, and 23.4%, respectively. CONCLUSIONS: Men diagnosed with clinically-localized prostate cancer in the contemporaneous PSA-screening era and treated with RALP achieve durable long-term oncological control. The data reported here (in a risk-stratified manner) represent the longest follow-up after robotic radical prostatectomy, and as such, should be of value when counseling patients regarding expected oncologic outcomes from RALP.

Trifecta and pentafecta outcomes following robot-assisted partial nephrectomy in a multi-institutional cohort of Indian patients.

Introduction: The literature on studies reporting trifecta or pentafecta outcomes following robot-assisted partial nephrectomy (RAPN) in Indian patients is limited. The primary aim of this study was to report and evaluate the factors predicting trifecta and pentafecta outcomes following RAPN in Indian patients using the multicentric Vattikuti collective quality initiative (VCQI) database. Methods: From the VCQI database for patients who underwent RAPN, data for Indian patients were extracted and analyzed for factors predicting the achievement of trifecta and pentafecta following RAPN. Trifecta was defined as the absence of complications, negative surgical margins, and warm ischemia period shorter than 25 min or zero ischemia. Pentafecta covers all the trifecta criteria as well as >90% preservation of estimated glomerular filtration rate (eGFR) and no stage upgrade of chronic kidney disease at 12 months. Results: In this study, among 614 patients, the trifecta was achieved in 374 patients (60.9%) and pentafecta was achieved in 24.2% of the patients. Patients who achieved trifecta had significantly higher mean age (54.1 vs. 51.0 years, P = 0.005), body mass index (BMI) (26.7 vs. 26.03 kg/m2, P = 0.022), and smaller tumor size (38.6 vs. 41.4 mm, P = 0.028). The preoperative eGFR (84.2 vs. 91.9 ml/min, P = 0.012) and renal nephrometry score (RNS) (6.96 vs. 7.87, P ≤ 0.0001) were significantly lower in the trifecta group. Comparing patients who achieved pentafecta to those who did not, we noted a statistically significant difference between the two groups for tumor size (36.1 vs. 41.5 mm, P = 0.017) and RNS (6.6 vs. 7.7, P = 0.0001). On multivariate analysis, BMI and RNS were associated with trifecta outcomes. Similarly, only RNS was identified as an independent predictor of pentafecta. Conclusions: RNS and BMI were independent predictors of the trifecta. At the same time, RNS was identified as an independent predictor of pentafecta following RAPN.

Development and Validation of a Nomogram Predicting Intraoperative Adverse Events During Robot-assisted Partial Nephrectomy.

BACKGROUND: Ability to predict the risk of intraoperative adverse events (IOAEs) for patients undergoing partial nephrectomy (PN) can be of great clinical significance. OBJECTIVE: To develop and internally validate a preoperative nomogram predicting IOAEs for robot-assisted PN (RAPN). DESIGN, SETTING, AND PARTICIPANTS: In this observational study, data for demographic, preoperative, and postoperative variables for patients who underwent RAPN were extracted from the Vattikuti Collective Quality Initiative (VCQI) database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: IOAEs were defined as the occurrence of intraoperative surgical complications, blood transfusion, or conversion to open surgery/radical nephrectomy. Backward stepwise logistic regression analysis was used to identify predictors of IOAEs. The nomogram was validated using bootstrapping, the area under the receiver operating characteristic curve (AUC), and the goodness of fit. Decision curve analysis (DCA) was used to determine the clinical utility of the model. RESULTS AND LIMITATIONS: Among the 2114 patients in the study cohort, IOAEs were noted in 158 (7.5%). Multivariable analysis identified five variables as independent predictors of IOAEs: RENAL nephrometry score (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.02-1.25); clinical tumor size (OR 1.01, 95% CI 1.001-1.024); PN indication as absolute versus elective (OR 3.9, 95% CI 2.6-5.7) and relative versus elective (OR 4.2, 95% CI 2.2-8); Charlson comorbidity index (OR 1.17, 95% CI 1.05-1.30); and multifocal tumors (OR 8.8, 95% CI 5.4-14.1). A nomogram was developed using these five variables. The model was internally valid on bootstrapping and goodness of fit. The AUC estimated was 0.76 (95% CI 0.72-0.80). DCA revealed that the model was clinically useful at threshold probabilities >5%. Limitations include the lack of external validation and selection bias. CONCLUSIONS: We developed and internally validated a nomogram predicting IOAEs during RAPN. PATIENT SUMMARY: We developed a preoperative model than can predict complications that might occur during robotic surgery for partial removal of a kidney. Tests showed that our model is fairly accurate and it could be useful in identifying patients with kidney cancer for whom this type of surgery is suitable.

Perioperative outcomes following robot-assisted partial nephrectomy for complex renal masses: A Vattikuti Collective Quality Initiative database study.

Introduction: Outcomes of robot-assisted partial nephrectomy (RAPN) depend on tumor complexity, surgeon experience and patient profile among other variables. We aimed to study the perioperative outcomes of RAPN for patients with complex renal masses using the Vattikuti Collective Quality Initiative (VCQI) database that allowed evaluation of multinational data. Methods: From the VCQI, we extracted data for all the patients who underwent RAPN with preoperative aspects and dimensions used for an anatomical (PADUA) score of ≥10. Multivariate logistic regression was conducted to ascertain predictors of trifecta (absence of complications, negative surgical margins, and warm ischemia times [WIT] <25 min or zero ischemia) outcomes. Results: Of 3,801 patients, 514 with PADUA scores ≥10 were included. The median operative time, WIT, and blood loss were 173 (range 45-546) min, 21 (range 0-55) min, and 150 (range 50-3500) ml, respectively. Intraoperative complications and blood transfusions were reported in 2.1% and 6%, respectively. In 8.8% of the patients, postoperative complications were noted, and surgical margins were positive in 10.3% of the patients. Trifecta could be achieved in 60.7% of patients. Clinical tumor size, duration of surgery, WIT, and complication rates were significantly higher in the group with a high (12 or 13) PADUA score while the trifecta was significantly lower in this group (48.4%). On multivariate analysis, surgical approach (retroperitoneal vs. transperitoneal) and high PADUA score (12/13) were identified as predictors of the trifecta outcomes. Conclusion: RAPN may be a reasonable surgical option for patients with complex renal masses with acceptable perioperative outcomes.

Perioperative outcomes following robot-assisted partial nephrectomy in elderly patients.

OBJECTIVE: To compare perioperative outcomes following robot-assisted partial nephrectomy (RAPN) in patients with age ≥ 70 years to age < 70 years. METHODS: Using Vattikuti Collective quality initiative (VCQI) database for RAPN we compared perioperative outcomes following RAPN between the two age groups. Primary outcome of the study was to compare trifecta outcomes between the two groups. Propensity matching using nearest neighbourhood method was performed with trifecta as primary outcome for sex, body mass index (BMI), solitary kidney, tumor size and Renal nephrometery score (RNS). RESULTS: Group A (age ≥ 70 years) included 461 patients whereas group B included 1932 patients. Before matching the two groups were statistically different for RNS and solitary kidney rates. After propensity matching, the two groups were comparable for baselines characteristics such as BMI, tumor size, clinical symptoms, tumor side, face of tumor, solitary kidney and tumor complexity. Among the perioperative outcome parameters there was no difference between two groups for operative time, blood loss, intraoperative transfusion, intraoperative complications, need for radical nephrectomy, positive margins and trifecta rates. Warm ischemia time was significantly longer in the younger age group (18.1 min vs. 16.3 min, p = 0.003). Perioperative complications were significantly higher in the older age group (11.8% vs. 7.7%, p = 0.041). However, there was no difference between the two groups for major complications. CONCLUSION: RAPN in well-selected elderly patients is associated with comparable trifecta outcomes with acceptable perioperative morbidity.

Comparison of perioperative outcomes following transperitoneal versus retroperitoneal robot-assisted partial nephrectomy: a propensity-matched analysis of VCQI database.

OBJECTIVE: To compare perioperative outcomes following retroperitoneal robot-assisted partial nephrectomy (RPRAPN) and transperitoneal robot-assisted partial nephrectomy (TPRAPN). METHODS: With this Vattikuti Collective Quality Initiative (VCQI) database, study propensity scores were calculated according to the surgical access (TPRAPN and RPRAPN) for the following independent variables, i.e., age, sex, side of the surgery, RENAL nephrometry scores (RNS), estimated glomerular filtration rate (eGFR) and serum creatinine. The study's primary outcome was the comparison of trifecta between the two groups. RESULTS: In this study, 309 patients who underwent RPRAPN were matched with 309 patients who underwent TPRAPN. The two groups matched well for age, sex, tumor side, polar location of the tumor, RNS, preoperative creatinine and eGFR. Operative time and warm ischemia time were significantly shorter with RPRAPN. Intraoperative blood loss and need for blood transfusion were lower with RPRAPN. There was a significantly higher number of intraoperative complications with RPRAPN. However, there was no difference in the two groups for postoperative complications. Trifecta outcomes were better with RPRAPN (70.2% vs. 53%, p < 0.0001) compared to TPRAPN. We noted no significant change in overall results when controlled for tumor location (anteriorly or posteriorly). The surgical approach, tumor size and RNS were identified as independent predictors of trifecta on multivariate analysis. CONCLUSION: RPRAPN is associated with superior perioperative outcomes in well-selected patients compared to TPRAPN. However, the data for the retroperitoneal approach were contributed by a few centers with greater experience with this technique, thus limiting the generalizability of the results of this study.

Outcomes in robot-assisted partial nephrectomy for imperative vs elective indications.

OBJECTIVES: To assess and compare peri-operative outcomes of patients undergoing robot-assisted partial nephrectomy (RAPN) for imperative vs elective indications. PATIENT AND METHODS: We retrospectively reviewed a multinational database of 3802 adults who underwent RAPN for elective and imperative indications. Laparoscopic or open partial nephrectomy (PN) were excluded. Baseline data for age, gender, body mass index, American Society of Anaesthesiologists score and PADUA score were examined. Patients undergoing RAPN for an imperative indication were matched to those having surgery for an elective indication using propensity scores in a 1:3 ratio. Primary outcomes included organ ischaemic time, operating time, estimated blood loss (EBL), rate of blood transfusions, Clavien-Dindo complications, conversion to radical nephrectomy (RN) and positive surgical margin (PSM) status. RESULTS: After propensity-score matching for baseline variables, a total of 304 patients (76 imperative vs 228 elective indications) were included in the final analysis. No significant differences were found between groups for ischaemia time (19.9 vs 19.8 min; P = 0.94), operating time (186 vs 180 min; P = 0.55), EBL (217 vs 190 mL; P = 0.43), rate of blood transfusions (2.7% vs 3.7%; P = 0.51), or Clavien-Dindo complications (P = 0.31). A 38.6% (SD 47.9) decrease in Day-1 postoperative estimated glomerular filtration rate was observed in the imperative indication group and an 11.3% (SD 45.1) decrease was observed in the elective indication group (P < 0.005). There were no recorded cases of permanent or temporary dialysis. There were no conversions to RN in the imperative group, and seven conversions (5.6%) in the elective group (P = 0.69). PSMs were seen in 1.4% (1/76) of the imperative group and in 3.3% of the elective group (7/228; P = 0.69). CONCLUSION: We conclude that RAPN is feasible and safe for imperative indications and demonstrates similar outcomes to those achieved for elective indications.

Robotic Kidney Transplantation with Regional Hypothermia versus Open Kidney Transplantation for Patients with End Stage Renal Disease: An Ideal Stage 2B Study.

PURPOSE: We report on comparative effectiveness of minimally invasive versus traditional open kidney transplantation. MATERIALS AND METHODS: We undertook a prospective cohort study of 654 patients who underwent open or robotic kidney transplantation at a single tertiary care hospital between January 2013 and December 2015. Primary outcome was delayed graft function, defined as the need for dialysis within 1 week of surgery. Secondary outcomes included postoperative complications, pain, graft rejection, and graft and patient survival. Nonparsimonious propensity score and Ding-VanderWeele analytical methods were used to account for confounding bias. RESULTS: Within the 1:3 matched cohort (robotic 126, open 378; well matched with standardized mean difference ∼10%), the robotic approach was associated with lower rates of wound infections (0% vs 4%, p=0.023) and symptomatic lymphoceles (0% vs 7% at 36 months, p=0.003), as well as reduced postoperative pain, requirement for narcotic analgesia and blood loss. There were no differences between the 2 groups, robotic versus open, with respect to graft function (delayed graft function 0% vs 2.4%, p=0.081), hospital stay (median 8 days for both, p=0.647), graft rejection (16.2% vs 18.6% at 36 months, p=0.643), and graft (95.2% vs 96.3% at 36 months, p=0.266) and overall survival (94.5% vs 98.1% at 36 months, p=0.307). Ding-VanderWeele analysis suggested minimal influence of unknown confounders on study findings. CONCLUSIONS: Robotic kidney transplantation with regional hypothermia was associated with a lower rate of postoperative complications and improved patient comfort in comparison to open kidney transplantation. Graft function, and graft and overall survival were comparable between the 2 techniques.

The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems.

Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic. These factors are threatening the environmental and economic sustainability of current and future food supply systems. Scientific and technological innovations are needed more than ever to secure enough food for a fast-growing global population. Scientific advances have led to a better understanding of how various components of the agricultural system interact, from the cell to the field level. Despite incredible advances in genetic tools over the past few decades, our ability to accurately assess crop status in the field, at scale, has been severely lacking until recently. Thanks to recent advances in remote sensing and Artificial Intelligence (AI), we can now quantify field scale phenotypic information accurately and integrate the big data into predictive and prescriptive management tools. This review focuses on the use of recent technological advances in remote sensing and AI to improve the resilience of agricultural systems, and we will present a unique opportunity for the development of prescriptive tools needed to address the next decade's agricultural and human nutrition challenges.

Omission of Cortical Renorrhaphy During Robotic Partial Nephrectomy: A Vattikuti Collective Quality Initiative Database Analysis.

OBJECTIVES: To analyze the outcomes of patients in whom cortical (outer) renorrhaphy (CR) was omitted during robotic partial nephrectomy (RPN). METHODS: We analyzed 1453 patients undergoing RPN, from 2006 to 2018, within a large multi-institutional database. Patients having surgery for bilateral tumors (n = 73) were excluded. CR and no-CR groups were compared in terms of operative and ischemia time, estimated blood loss (EBL), complications, surgical margins, hospital stay, change in estimated glomerular filtration rate (eGFR), and need of angioembolization. Inverse probability of treatment weighting with Firth correction for center code was performed to account for selection bias. RESULTS: CR was omitted in 120 patients (8.7%); 1260 (91.3%) patients underwent both inner layer and CR. There was no difference in intraoperative complications (7.4% CR; 8.9% no-CR group; P = .6), postoperative major complications (1% and 2.8% in CR and no-CR groups, respectively; P = .2), or median drop in eGFR (7.3 vs 10.4 mL/min/m2). The no-CR group had a higher incidence of minor complications (26.7% vs 5.5% in CR group; P < .001). EBL was 100 mL (IQR 50-200) in both groups (P = .6). Angioembolization was needed in 0.7% patients in CR vs 1.4% in no-CR group (P = .4). Additionally, there was no difference in median operative time (168 vs 162 min; P = .2) or ischemia time (18 vs 17 min; P = .7). CONCLUSION: In selected patients with renal masses, single layer renorrhaphy does not significantly improve operative time, ischemia time, or eGFR after RPN. There is a higher incidence of minor complications, but not major perioperative complications after no-CR technique.

Urological implications of SARS CoV-19.

INTRODUCTION: The novel coronavirus disease 2019 (COVID-19), pandemic has afflicted > 3.3 million people around the world since December 2019. Though, more than 1000 publications have appeared in scientific journals addressing a plethora of questions, there is a considerable hiatus in understanding of the behavior and natural history of the virus and its impact on urology. Also, a modified approach is the need of hour in taking care of patients as urologists should safeguard their teams, families, and patients. MATERIAL AND METHODS: The authors have used guidelines from USA, Canada, UK, Europe and India for making recommendations to help urologist define their own policies that may have to be fine-tuned on the basis of continued and evolving challenges they would encounter and the local resources at their disposal. RESULTS: COVID-19 do effect genitourinary system from kidney to testis. The authors provide scientific basis to urologists to help identify patients by remote consultation who are likely to be harmed by coming to the hospital, and not to miss those who need hospitalization for diagnostic or therapeutic interventions. There is uncompromised need of specific precautions during surgery to safe guard the surgeon and his team along with the patient. CONCLUSIONS: Urological operations during COVID-19 pandemic should be limited to emergency cases during the acute phase with an exit strategy planned in a staggered manner, based on the scientific risk stratification. Telemedicine (e-clinics or virtual clinics) would help achieve the goal of risk stratification.

Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study.

OBJECTIVE: To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. MATERIALS AND METHODS: The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC). RESULTS: The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC-ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR-AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC-ROC of 0.875 (95% CI 0.834, 0.913) and a PR-AUC 0.706 (95% CI, 0.610, 0.790). CONCLUSIONS: The performance of the ML models in the present study was encouraging. Further validation in a multi-institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes.

Artificial intelligence and robotic surgery: current perspective and future directions.

PURPOSE OF REVIEW: This review aims to draw a road-map to the use of artificial intelligence in an era of robotic surgery and highlight the challenges inherent to this process. RECENT FINDINGS: Conventional mechanical robots function by transmitting actions of the surgeon's hands to the surgical target through the tremor-filtered movements of surgical instruments. Similarly, the next iteration of surgical robots conform human-initiated actions to a personalized surgical plan leveraging 3D digital segmentation generated prior to surgery. The advancements in cloud computing, big data analytics, and artificial intelligence have led to increased research and development of intelligent robots in all walks of human life. Inspired by the successful application of deep learning, several surgical companies are joining hands with tech giants to develop intelligent surgical robots. We, hereby, highlight key steps in the handling and analysis of big data to build, define, and deploy deep-learning models for building autonomous robots. SUMMARY: Despite tremendous growth of autonomous robotics, their entry into the operating room remains elusive. It is time that surgeons actively collaborate for the development of the next generation of intelligent robotic surgery.