Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Objective: To evaluate preoperative disability’s influence on patient-reported outcomes (PROs) following surgery for degenerative spondylolisthesis (DS). Methods: DS patients who underwent surgical intervention were retrospectively identified from a single-surgeon spine registry. Cohorts based on Oswestry Disability Index (ODI) < 41 (milder disability) and ≥ 41 (severe disability) were created. Demographic differences were accounted for with 1:1 propensity score matching. For the matched sample, perioperative and PRO data were additionally collected. PROs assessed included mental health, physical function, pain, and disability. Pre- and up to 2-year postoperative PROs were utilized. Average time to final follow-up was 15.7 ± 8.8 months. Improvements in PROs and minimal clinically important difference (MCID) rates were calculated. Continuous variables were compared through Student t-test and categorical variables were compared through chi-square tests. Results: Altogether, 214 patients were included with 77 in the milder disability group. The severe disability group had worse postoperative day (POD) 1 pain scores and longer hospital stays (p ≤ 0.038, both). The severe disability group reported worse outcomes pre- and postoperatively (p < 0.011, all), but had greater average improvement in 12-item Short Form health survey mental composite score (SF-12 MCS), 9-Item Patient Health Questionnaire (PHQ-9), visual analogue scale (VAS)-back, and ODI by 6 weeks (p ≤ 0.037, all) and PHQ-9, VAS-back and ODI by final follow-up (p ≤ 0.015, all). The severe disability cohort was more likely to achieve MCID for SF-12 MCS, PHQ-9, and ODI (p ≤ 0.003, all). Conclusion Patients with greater baseline disability report higher POD 1 pain and discharge later than patients with milder disability. While these patients report inferior physical/mental health before and after surgery, they report greater improvements in mental health and disability postoperatively.

Objective: To evaluate the effect of Veterans RAND 12-item health survey mental composite score (VR-12 MCS) on postoperative patient-reported outcome measures (PROMs) after undergoing lateral lumbar interbody fusion. Methods: Retrospective data from a single-surgeon database created 2 cohorts: patients with VR-12 MCS ≥ 50 or VR-12 MCS < 50. Preoperative, 6-week, and final follow-up (FF)- PROMs including VR-12 MCS/physical composite score (PCS), 12-item Short Form health survey (SF-12) MCS/PCS, Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS-PF), Patient Health Questionnaire-9 (PHQ-9), visual analogue scale (VAS)-back/leg pain (VAS-BP/LP), and Oswestry Disability Index (ODI) were collected. ∆6-week and ∆FF-PROMs were calculated. Minimal clinically important difference (MCID) achievement rates were determined from established cutoffs from the literature. For intercohort comparison, chi-square analysis was used for categorical variables, and Student t-test for continuous variables. Results: Seventy-nine patients were included; 25 were in VR-12 MCS < 50. Mean postoperative follow-up time was 17.12 ± 8.43 months. The VR-12 MCS < 50 cohort had worse VR-12 PCS, SF-12 MCS, PROMIS-PF, PHQ-9, VAS-BP, and ODI scores preoperatively (p ≤ 0.014, all), worse VR-12 MCS/PCS, SF-12 MCS, PROMIS-PF, PHQ-9, and ODI scores at 6-week postoperatively (p ≤ 0.039, all), and worse VR-12 MCS, SF-12 MCS, PROMIS-PF, PHQ-9, VAS-BP, VAS-LP, and ODI scores at FF (p ≤ 0.046, all). The VR-12 MCS < 50 cohort showed greater improvement in VR-12 MCS and SF-12 MCS scores at 6 weeks and FF (p ≤ 0.005, all). The VR-12 MCS < 50 cohort experienced greater MCID achievement for VR-12 MCS, SF-12 MCS, and PHQ-9 (p ≤ 0.006, all). Conclusion VR-12 MCS < 50 yielded worse mental health, physical function, pain and disability postoperatively, yet reported greater improvements in magnitude and MCID achievement for mental health.

Objective: To evaluate preoperative disability’s influence on patient-reported outcomes (PROs) following surgery for degenerative spondylolisthesis (DS). Methods: DS patients who underwent surgical intervention were retrospectively identified from a single-surgeon spine registry. Cohorts based on Oswestry Disability Index (ODI) < 41 (milder disability) and ≥ 41 (severe disability) were created. Demographic differences were accounted for with 1:1 propensity score matching. For the matched sample, perioperative and PRO data were additionally collected. PROs assessed included mental health, physical function, pain, and disability. Pre- and up to 2-year postoperative PROs were utilized. Average time to final follow-up was 15.7 ± 8.8 months. Improvements in PROs and minimal clinically important difference (MCID) rates were calculated. Continuous variables were compared through Student t-test and categorical variables were compared through chi-square tests. Results: Altogether, 214 patients were included with 77 in the milder disability group. The severe disability group had worse postoperative day (POD) 1 pain scores and longer hospital stays (p ≤ 0.038, both). The severe disability group reported worse outcomes pre- and postoperatively (p < 0.011, all), but had greater average improvement in 12-item Short Form health survey mental composite score (SF-12 MCS), 9-Item Patient Health Questionnaire (PHQ-9), visual analogue scale (VAS)-back, and ODI by 6 weeks (p ≤ 0.037, all) and PHQ-9, VAS-back and ODI by final follow-up (p ≤ 0.015, all). The severe disability cohort was more likely to achieve MCID for SF-12 MCS, PHQ-9, and ODI (p ≤ 0.003, all). Conclusion Patients with greater baseline disability report higher POD 1 pain and discharge later than patients with milder disability. While these patients report inferior physical/mental health before and after surgery, they report greater improvements in mental health and disability postoperatively.

Objective: To evaluate the effect of Veterans RAND 12-item health survey mental composite score (VR-12 MCS) on postoperative patient-reported outcome measures (PROMs) after undergoing lateral lumbar interbody fusion. Methods: Retrospective data from a single-surgeon database created 2 cohorts: patients with VR-12 MCS ≥ 50 or VR-12 MCS < 50. Preoperative, 6-week, and final follow-up (FF)- PROMs including VR-12 MCS/physical composite score (PCS), 12-item Short Form health survey (SF-12) MCS/PCS, Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS-PF), Patient Health Questionnaire-9 (PHQ-9), visual analogue scale (VAS)-back/leg pain (VAS-BP/LP), and Oswestry Disability Index (ODI) were collected. ∆6-week and ∆FF-PROMs were calculated. Minimal clinically important difference (MCID) achievement rates were determined from established cutoffs from the literature. For intercohort comparison, chi-square analysis was used for categorical variables, and Student t-test for continuous variables. Results: Seventy-nine patients were included; 25 were in VR-12 MCS < 50. Mean postoperative follow-up time was 17.12 ± 8.43 months. The VR-12 MCS < 50 cohort had worse VR-12 PCS, SF-12 MCS, PROMIS-PF, PHQ-9, VAS-BP, and ODI scores preoperatively (p ≤ 0.014, all), worse VR-12 MCS/PCS, SF-12 MCS, PROMIS-PF, PHQ-9, and ODI scores at 6-week postoperatively (p ≤ 0.039, all), and worse VR-12 MCS, SF-12 MCS, PROMIS-PF, PHQ-9, VAS-BP, VAS-LP, and ODI scores at FF (p ≤ 0.046, all). The VR-12 MCS < 50 cohort showed greater improvement in VR-12 MCS and SF-12 MCS scores at 6 weeks and FF (p ≤ 0.005, all). The VR-12 MCS < 50 cohort experienced greater MCID achievement for VR-12 MCS, SF-12 MCS, and PHQ-9 (p ≤ 0.006, all). Conclusion VR-12 MCS < 50 yielded worse mental health, physical function, pain and disability postoperatively, yet reported greater improvements in magnitude and MCID achievement for mental health.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Objective: To evaluate preoperative disability’s influence on patient-reported outcomes (PROs) following surgery for degenerative spondylolisthesis (DS). Methods: DS patients who underwent surgical intervention were retrospectively identified from a single-surgeon spine registry. Cohorts based on Oswestry Disability Index (ODI) < 41 (milder disability) and ≥ 41 (severe disability) were created. Demographic differences were accounted for with 1:1 propensity score matching. For the matched sample, perioperative and PRO data were additionally collected. PROs assessed included mental health, physical function, pain, and disability. Pre- and up to 2-year postoperative PROs were utilized. Average time to final follow-up was 15.7 ± 8.8 months. Improvements in PROs and minimal clinically important difference (MCID) rates were calculated. Continuous variables were compared through Student t-test and categorical variables were compared through chi-square tests. Results: Altogether, 214 patients were included with 77 in the milder disability group. The severe disability group had worse postoperative day (POD) 1 pain scores and longer hospital stays (p ≤ 0.038, both). The severe disability group reported worse outcomes pre- and postoperatively (p < 0.011, all), but had greater average improvement in 12-item Short Form health survey mental composite score (SF-12 MCS), 9-Item Patient Health Questionnaire (PHQ-9), visual analogue scale (VAS)-back, and ODI by 6 weeks (p ≤ 0.037, all) and PHQ-9, VAS-back and ODI by final follow-up (p ≤ 0.015, all). The severe disability cohort was more likely to achieve MCID for SF-12 MCS, PHQ-9, and ODI (p ≤ 0.003, all). Conclusion Patients with greater baseline disability report higher POD 1 pain and discharge later than patients with milder disability. While these patients report inferior physical/mental health before and after surgery, they report greater improvements in mental health and disability postoperatively.

Objective: To evaluate the effect of Veterans RAND 12-item health survey mental composite score (VR-12 MCS) on postoperative patient-reported outcome measures (PROMs) after undergoing lateral lumbar interbody fusion. Methods: Retrospective data from a single-surgeon database created 2 cohorts: patients with VR-12 MCS ≥ 50 or VR-12 MCS < 50. Preoperative, 6-week, and final follow-up (FF)- PROMs including VR-12 MCS/physical composite score (PCS), 12-item Short Form health survey (SF-12) MCS/PCS, Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS-PF), Patient Health Questionnaire-9 (PHQ-9), visual analogue scale (VAS)-back/leg pain (VAS-BP/LP), and Oswestry Disability Index (ODI) were collected. ∆6-week and ∆FF-PROMs were calculated. Minimal clinically important difference (MCID) achievement rates were determined from established cutoffs from the literature. For intercohort comparison, chi-square analysis was used for categorical variables, and Student t-test for continuous variables. Results: Seventy-nine patients were included; 25 were in VR-12 MCS < 50. Mean postoperative follow-up time was 17.12 ± 8.43 months. The VR-12 MCS < 50 cohort had worse VR-12 PCS, SF-12 MCS, PROMIS-PF, PHQ-9, VAS-BP, and ODI scores preoperatively (p ≤ 0.014, all), worse VR-12 MCS/PCS, SF-12 MCS, PROMIS-PF, PHQ-9, and ODI scores at 6-week postoperatively (p ≤ 0.039, all), and worse VR-12 MCS, SF-12 MCS, PROMIS-PF, PHQ-9, VAS-BP, VAS-LP, and ODI scores at FF (p ≤ 0.046, all). The VR-12 MCS < 50 cohort showed greater improvement in VR-12 MCS and SF-12 MCS scores at 6 weeks and FF (p ≤ 0.005, all). The VR-12 MCS < 50 cohort experienced greater MCID achievement for VR-12 MCS, SF-12 MCS, and PHQ-9 (p ≤ 0.006, all). Conclusion VR-12 MCS < 50 yielded worse mental health, physical function, pain and disability postoperatively, yet reported greater improvements in magnitude and MCID achievement for mental health.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Objective: To evaluate preoperative disability’s influence on patient-reported outcomes (PROs) following surgery for degenerative spondylolisthesis (DS). Methods: DS patients who underwent surgical intervention were retrospectively identified from a single-surgeon spine registry. Cohorts based on Oswestry Disability Index (ODI) < 41 (milder disability) and ≥ 41 (severe disability) were created. Demographic differences were accounted for with 1:1 propensity score matching. For the matched sample, perioperative and PRO data were additionally collected. PROs assessed included mental health, physical function, pain, and disability. Pre- and up to 2-year postoperative PROs were utilized. Average time to final follow-up was 15.7 ± 8.8 months. Improvements in PROs and minimal clinically important difference (MCID) rates were calculated. Continuous variables were compared through Student t-test and categorical variables were compared through chi-square tests. Results: Altogether, 214 patients were included with 77 in the milder disability group. The severe disability group had worse postoperative day (POD) 1 pain scores and longer hospital stays (p ≤ 0.038, both). The severe disability group reported worse outcomes pre- and postoperatively (p < 0.011, all), but had greater average improvement in 12-item Short Form health survey mental composite score (SF-12 MCS), 9-Item Patient Health Questionnaire (PHQ-9), visual analogue scale (VAS)-back, and ODI by 6 weeks (p ≤ 0.037, all) and PHQ-9, VAS-back and ODI by final follow-up (p ≤ 0.015, all). The severe disability cohort was more likely to achieve MCID for SF-12 MCS, PHQ-9, and ODI (p ≤ 0.003, all). Conclusion Patients with greater baseline disability report higher POD 1 pain and discharge later than patients with milder disability. While these patients report inferior physical/mental health before and after surgery, they report greater improvements in mental health and disability postoperatively.