Purpose: The aim of this study was to identify, compile, and report the technical specifications of current and historical intraoral digital radiographic systems and recommend standardised reporting practices for production companies. Materials and Methods: A comprehensive report was prepared on 150 intraoral digital radiographic systems, comprising 105 sensor-based (70%) and 45 phosphor storage plate (PSP)-based systems (30%). Technical specifi cations were obtained from official company sources and scientific articles to ensure a complete collection of available data. Results: These systems were produced by 55 companies across 11 countries, with the United States leading (35.3%), followed by France (12%). Among the sensor systems, 76.2% used complementary metal-oxide-semiconductor (CMOS) technology, with notable variations in sizes and resolutions. PSP systems were available in 7 plate sizes and displayed diverse resolutions and scanning times. Twenty-one companies produced both sensor- and PSP-based systems, 33 produced only sensor-based systems, and 1 produced exclusively PSP-based systems. Conclusion This report identified 150 digital radiographic systems, revealing wide variability in technicalspecifications and a lack of standardised reporting protocols. The comprehensive summary and recommendations forconsistent documentation provided here can help clinicians make informed decisions and encourage manufacturers and production companies to adopt uniform reporting standards aligned with local regulatory frameworks.

Purpose: The aim of this study was to identify, compile, and report the technical specifications of current and historical intraoral digital radiographic systems and recommend standardised reporting practices for production companies. Materials and Methods: A comprehensive report was prepared on 150 intraoral digital radiographic systems, comprising 105 sensor-based (70%) and 45 phosphor storage plate (PSP)-based systems (30%). Technical specifi cations were obtained from official company sources and scientific articles to ensure a complete collection of available data. Results: These systems were produced by 55 companies across 11 countries, with the United States leading (35.3%), followed by France (12%). Among the sensor systems, 76.2% used complementary metal-oxide-semiconductor (CMOS) technology, with notable variations in sizes and resolutions. PSP systems were available in 7 plate sizes and displayed diverse resolutions and scanning times. Twenty-one companies produced both sensor- and PSP-based systems, 33 produced only sensor-based systems, and 1 produced exclusively PSP-based systems. Conclusion This report identified 150 digital radiographic systems, revealing wide variability in technicalspecifications and a lack of standardised reporting protocols. The comprehensive summary and recommendations forconsistent documentation provided here can help clinicians make informed decisions and encourage manufacturers and production companies to adopt uniform reporting standards aligned with local regulatory frameworks.

Purpose: The aim of this study was to identify, compile, and report the technical specifications of current and historical intraoral digital radiographic systems and recommend standardised reporting practices for production companies. Materials and Methods: A comprehensive report was prepared on 150 intraoral digital radiographic systems, comprising 105 sensor-based (70%) and 45 phosphor storage plate (PSP)-based systems (30%). Technical specifi cations were obtained from official company sources and scientific articles to ensure a complete collection of available data. Results: These systems were produced by 55 companies across 11 countries, with the United States leading (35.3%), followed by France (12%). Among the sensor systems, 76.2% used complementary metal-oxide-semiconductor (CMOS) technology, with notable variations in sizes and resolutions. PSP systems were available in 7 plate sizes and displayed diverse resolutions and scanning times. Twenty-one companies produced both sensor- and PSP-based systems, 33 produced only sensor-based systems, and 1 produced exclusively PSP-based systems. Conclusion This report identified 150 digital radiographic systems, revealing wide variability in technicalspecifications and a lack of standardised reporting protocols. The comprehensive summary and recommendations forconsistent documentation provided here can help clinicians make informed decisions and encourage manufacturers and production companies to adopt uniform reporting standards aligned with local regulatory frameworks.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Conclusion Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.