Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06634472
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-10-02
Brief Title:
Artificial Intelligence-Enabled Skin Perforator Segmentation
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Validation of an Artificial Intelligence-Enabled Skin Perforator Segmentation Tool in Computer-Assisted Osteocutaneous Fibular Free Flap Harvest a Clinical Trial
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Computer-assisted surgery CAS has revolutionised head and neck reconstruction with more efficient accurate and predictable surgery as reported in our previous studies Skin perforators are perforating vessels that travel through muscles and septa to supply the skin The identification of skin perforators is crucial for a safe fibula osteocutaneous free flap FFF harvest with CAS Different methods have been proposed in the past each of which has its own limitations
Traditionally skin perforators are identified with a Doppler ultrasound Berrone et al measured the locations with a Doppler and imported the information back to guide virtual surgical planning VSP However their study showed imprecise concordance between handheld Doppler measurements and the actual perforator locations good correlation between the location of perforators and bone segments was identified in only four out of six cases investigated To improve on the accuracy computed tomography angiography CTA was used for skin perforator identification Battaglia et al manually marked the perforating vessel location at the subcutaneous level and reported good correlation However the manual segmentation of the perforator was at the subcutaneous level only The course of the perforators which would be more significant for the design of CAS FFF harvest was not shown
To incorporate the course of skin perforators into FFF VSP Ettinger et al first described the technique of manual tracing from CTA in 2018 and validated its accuracy in 2022 The median absolute difference between the CTA and intraoperative measurements was 3 mm However reports quoted an average of 2 to 3 hours spent on tracing and modelling the course of the perforators depending on their number and anatomy consequently this adds significant burden to healthcare professionals
Recently United imaging intelligence has developed an AI-based program that offers a potential solution for accurate and efficient localisation of skin perforators to be incorporated into the current VSP workflow The proposed study aims to validate its performance in a prospective case series This will be the first study to investigate the use of AI-enabled program for fibula skin paddle perforator identification
Traditionally skin perforators are identified with a Doppler ultrasound Berrone et al measured the locations with a Doppler and imported the information back to guide virtual surgical planning VSP However their study showed imprecise concordance between handheld Doppler measurements and the actual perforator locations good correlation between the location of perforators and bone segments was identified in only four out of six cases investigated To improve on the accuracy computed tomography angiography CTA was used for skin perforator identification Battaglia et al manually marked the perforating vessel location at the subcutaneous level and reported good correlation However the manual segmentation of the perforator was at the subcutaneous level only The course of the perforators which would be more significant for the design of CAS FFF harvest was not shown
To incorporate the course of skin perforators into FFF VSP Ettinger et al first described the technique of manual tracing from CTA in 2018 and validated its accuracy in 2022 The median absolute difference between the CTA and intraoperative measurements was 3 mm However reports quoted an average of 2 to 3 hours spent on tracing and modelling the course of the perforators depending on their number and anatomy consequently this adds significant burden to healthcare professionals
Recently United imaging intelligence has developed an AI-based program that offers a potential solution for accurate and efficient localisation of skin perforators to be incorporated into the current VSP workflow The proposed study aims to validate its performance in a prospective case series This will be the first study to investigate the use of AI-enabled program for fibula skin paddle perforator identification
Detailed Description:
Aims and Hypotheses to be Tested
The aim of this single-arm clinical trial is to validate the performance of the AI-enabled skin perforator segmentation tool AI tool in computer-assisted FFF harvest
To test the predictive accuracy PA of the AI tool in identifying the targeted skin perforators primary endpoint
The primary outcome of the PA is calculated from the number of truefalse positive TPFP truefalse negative TNFN targeted perforators
The secondary outcomes include the sensitivity SEN specificity SPE positive and negative predictive values PPV and NPV respectively
Our hypothesis is that the AI-enabled vessel segmentation tool will demonstrate a high level of accuracy in identifying the skin perforators during the CAS osteocutaneous FFF harvest
Plan of Investigation
i Study design
The current study is a single-arm single-centre clinical trial to explore the performance of an AI-enabled skin perforator segmentation tool in CAS FFF harvest The study protocol shall conform to Standard Protocol Items Recommendations for Interventional Trials SPIRIT and Practical Robust Implementation and Sustainability Model PRISM
Data collection procedure A case report form CRF attached in the Appendix has been designed to collect pre- and intra-operative research data
Patient demographics will be collected including age gender body height weight body mass index BMI diagnosis medical history and smoking status
Preoperative measurements after incorporating the AI-segmented skin perforators to the VSP will also be obtained including but not limited to
Total fibula length
Number and lengths of fibula segments
Number of immediate dental implants
Number of perforators
Type of perforators
1 Septocutaneous vs musculocutaneous
2 Targeted to be incorporated into the VSP and harvesting vs accessory other perforators
Relative location of the perforators measured as the distance from the most inferior point of the lateral malleolus to
1 Level of the perforator as it crosses the posterior aspect of the fibula
2 The point where it bifurcates from the peroneal artery
Time spent incorporating the skin perforator model to the original VSP workflow
Intraoperatively the following information will be recorded in CRF
Deviation between the locations of the skin perforators identified by the AI tool and during the surgery at the point where it crosses the posterior edge of the fibula
Need for intraoperative modifications of the VSP due to the perforator identification discrepancy
Operator satisfaction with the AI-segmentation tool
The primary endpoint is the predictive accuracy of the AI-enabled skin perforator segmentation tool It will be calculated according to the definition by Šimundić et al When the skin perforator is identified by both the AI-segmentation tool and during the surgery it will be counted as a true positive TP When the perforator is identified by the AI-segmentation tool but not found during the surgery it is counted as a false positive FP When the perforator is seen during the surgery but not shown by the AI tool it is a false negative FN Finally a true negative TN perforator count will be derived from those subjects who do not exhibit an FN perforator The predictive accuracy PA is identified as the percentage of true perforators among all the perforators and is calculated as TP TNTPFPTNFN100
The secondary outcomes of sensitivity SEN specificity SPE positive and negative predictive values PPV and NPV respectively of the AI segmentation tool will be calculated according to the definition given in Table 1 These outcomes will be calculated at the perforator trunk for all the perforators when the perforators cross the posterior edge of the fibula and at the branches for the targeted perforators when they enter the subcutaneous layer of skin
ii Methods
Prof Pu and Prof Su have been working on computer assisted jaw reconstruction virtual surgical planning for several years The workflow has already been set up for the previously funded HMRF projects The efficiency predictability and accuracy of the protocol have been demonstrated in previously published articles
For the currently proposed project the investigators aim to incorporate the AI tool into the current VSP workflow and assess its performance The complete process will be divided into three parts 1 preoperative preparation 2 surgery and intra-operative recording 3 postoperative revaluation In the preliminary study the investigators have established the feasibility of the methodology via several pilot cases
1 Preoperative preparations
Preoperative computed tomography CT imaging Patients enrolled in the study will be arranged to undergo a CT scan on a General Electric revolution system GE Healthcare Chicago IL with slice thicknesses of 0625 mm for both the head and neck and lower limbs Iodinated contrast medium Iopamiro 370 Bracco Israel at a concentration of 370 mgml will be administered intravenously at a flow rate of 4 to 5 mlsec CT angiogram of the lower extremity will be taken at the arterial phase The imaging data will be exported in the Digital Imaging and Communications in Medicine DICOM format
AI-enabled segmentation of skin perforators from CT angiogram The DICOM data of the CTA will be imported to the AI workstation United Imaging Intelligence UII Shanghai China The tibia and fibula bone skin blood vessels of the lower extremities including the anterior tibial artery ATA posterior tibial artery PTA and peroneal artery PEA will be segmented with pre-set threshold range to generate the stereolithography stl models Based on the fully CNN of V-net the blood vessel branches from the PEA will be automatically segmented to build the stl model The small perforating vessels from the PEA traveling laterally crossing the posterior edge of the fibula bone to the skin surface will be marked as skin perforators The stl models of the fibula bone skin ATA PTA PEA and skin perforators will be exported from the workstation for incorporation into the VSP workflow
Virtual surgery planning
Preoperative imaging and building of models Preoperative CT scan will be imported to ProPlan CMF 21 software Materialise Leuven Belgium Volume segmentation will be performed to build the 3D virtual models of the jaws
Incorporating the skin perforators The stl models exported from the AI workstation will be imported to the ProPlan file The fibula length and skin perforator location will be measured and recorded
Virtual fibula flap harvest segmentation and dental implant insertion VSP will be performed using the methodology described in the previous publications The extent of resection will be pre-determined based on the type and extension of the pathology The osteotomies for fibula harvest and segmentation will be planned to suit the defect after guided tumour resection The location of the immediate dental implants will be marked in the fibula segments Taking the location of the skin perforators into consideration the osteotomies can be adjusted to include the take-off point of the skin perforators while avoiding placing the perforator directly in planned osteotomies or implant insertion points
Design and fabrication of surgical guides andor plates After the VSP is confirmed the surgical guides andor plates will be designed using 3-matic 130 software Materialise Leuven Belgium The location of the perforators as they cross the posterior edge of the fibula will be marked with a small sphere on the fibula harvest guide The guides will be printed with biocompatible and autoclavable resin - either MED610 Stratasys Ltd Eden Prairie MN USA or NextDent SG Vertex Dental The Netherlands
2 Surgical procedures and intraoperative recording All the surgical procedures will be conducted in the routine manner of CAS jaw reconstruction using FFF by the PA Briefly osteotomies direct dental implant insertion bone movements and bone inset will be guided by the prepared surgical templates
Intraoperative measurements will be conducted Any FP or FN perforators will be recorded Any unanticipated event that requires the modification of the fibula harvest plan will be recorded Standard perioperative management will be conducted in a routine manner
3 Postoperative evaluation Postoperative evaluation will be conducted between the surgical team led by the PA and the computer science team led by the Co-A Any intraoperative events will be discussed in a timely manner Data entry will be performed for each case right after the surgery
iii Sample size The calculation of the sample size is determined by the primary endpoint which focuses on the predictive accuracy of the AI tool in identifying the targeted skin perforators As the success rate of the conventional method in identifying a reliable correlation was 67 the aim of the AI-segmentation tool is to achieve a minimum accuracy of 80 A one-sided test for non-inferioritysuperiority is employed The sample size is calculated using the exact binomial test for a one-sample non-inferioritysuperiority clinical study The hypothesis can be unified as H0 p p0 δ versus Ha p p1 Here δ represents the intervention effect size p0 equals 80 and p is the true accuracy which is hypothesised to exceed 80 with a statistical power of at least 90 with a 5 level of significance
According to a previous report on the AI-enabled skin perforator segmentation tool the diagnostic accuracy was reported as 97 The investigators can conservatively estimate the true success rate as 95 representing an intervention effect size of 15 Based on the calculation a sample size of 44 is expected to have a 933 statistical power Assuming at least one targeted perforator for each case and a drop-out rate after signing the informed consent of 10 a total of 49 patients will be recruited to the study
iv Data processing and analysis
All the data in this protocol will be recorded using a specially designed CRF Appendix Discrepancies in the database will be traced to the original documents for resolution All records will be audited 100 for accuracy Data entry will be performed by two independent assistants using the IBM SPSS Statistics Version 25 Consistency of input data between assistants will be compared using a inbuilt function
For the demographic information data will be presented as mean values with standard deviations for continuous data and as counts with proportions for categorical data The predictive accuracy will be calculated and presented as frequency The one-sided 95 binomial proportion confidence interval will be computed and the lower confidence limit will be compared with 80 The sensitivity specificity and positive and negative predictive values will be calculated Continuous data distance of perforators identified by the AI tool and during the surgeries location of the perforators relative to the total fibula length size of the perforators time and satisfaction measurements etc will be expressed as mean values with standard deviation SD for normally distributed data and expressed as the median with interquartile range IQR or range for skewed data To evaluate the effect of various confounding variables on the precision of perforators identified by AI suitable tests such as Pearson or Spearmans correlation test t-Test or Wilcoxon Signed-rank test will be selected depending on the normality of the data Statistical significance will be set at p 005 All statistical analysis will be performed using IBM SPSS Statistics Version 25
The aim of this single-arm clinical trial is to validate the performance of the AI-enabled skin perforator segmentation tool AI tool in computer-assisted FFF harvest
To test the predictive accuracy PA of the AI tool in identifying the targeted skin perforators primary endpoint
The primary outcome of the PA is calculated from the number of truefalse positive TPFP truefalse negative TNFN targeted perforators
The secondary outcomes include the sensitivity SEN specificity SPE positive and negative predictive values PPV and NPV respectively
Our hypothesis is that the AI-enabled vessel segmentation tool will demonstrate a high level of accuracy in identifying the skin perforators during the CAS osteocutaneous FFF harvest
Plan of Investigation
i Study design
The current study is a single-arm single-centre clinical trial to explore the performance of an AI-enabled skin perforator segmentation tool in CAS FFF harvest The study protocol shall conform to Standard Protocol Items Recommendations for Interventional Trials SPIRIT and Practical Robust Implementation and Sustainability Model PRISM
Data collection procedure A case report form CRF attached in the Appendix has been designed to collect pre- and intra-operative research data
Patient demographics will be collected including age gender body height weight body mass index BMI diagnosis medical history and smoking status
Preoperative measurements after incorporating the AI-segmented skin perforators to the VSP will also be obtained including but not limited to
Total fibula length
Number and lengths of fibula segments
Number of immediate dental implants
Number of perforators
Type of perforators
1 Septocutaneous vs musculocutaneous
2 Targeted to be incorporated into the VSP and harvesting vs accessory other perforators
Relative location of the perforators measured as the distance from the most inferior point of the lateral malleolus to
1 Level of the perforator as it crosses the posterior aspect of the fibula
2 The point where it bifurcates from the peroneal artery
Time spent incorporating the skin perforator model to the original VSP workflow
Intraoperatively the following information will be recorded in CRF
Deviation between the locations of the skin perforators identified by the AI tool and during the surgery at the point where it crosses the posterior edge of the fibula
Need for intraoperative modifications of the VSP due to the perforator identification discrepancy
Operator satisfaction with the AI-segmentation tool
The primary endpoint is the predictive accuracy of the AI-enabled skin perforator segmentation tool It will be calculated according to the definition by Šimundić et al When the skin perforator is identified by both the AI-segmentation tool and during the surgery it will be counted as a true positive TP When the perforator is identified by the AI-segmentation tool but not found during the surgery it is counted as a false positive FP When the perforator is seen during the surgery but not shown by the AI tool it is a false negative FN Finally a true negative TN perforator count will be derived from those subjects who do not exhibit an FN perforator The predictive accuracy PA is identified as the percentage of true perforators among all the perforators and is calculated as TP TNTPFPTNFN100
The secondary outcomes of sensitivity SEN specificity SPE positive and negative predictive values PPV and NPV respectively of the AI segmentation tool will be calculated according to the definition given in Table 1 These outcomes will be calculated at the perforator trunk for all the perforators when the perforators cross the posterior edge of the fibula and at the branches for the targeted perforators when they enter the subcutaneous layer of skin
ii Methods
Prof Pu and Prof Su have been working on computer assisted jaw reconstruction virtual surgical planning for several years The workflow has already been set up for the previously funded HMRF projects The efficiency predictability and accuracy of the protocol have been demonstrated in previously published articles
For the currently proposed project the investigators aim to incorporate the AI tool into the current VSP workflow and assess its performance The complete process will be divided into three parts 1 preoperative preparation 2 surgery and intra-operative recording 3 postoperative revaluation In the preliminary study the investigators have established the feasibility of the methodology via several pilot cases
1 Preoperative preparations
Preoperative computed tomography CT imaging Patients enrolled in the study will be arranged to undergo a CT scan on a General Electric revolution system GE Healthcare Chicago IL with slice thicknesses of 0625 mm for both the head and neck and lower limbs Iodinated contrast medium Iopamiro 370 Bracco Israel at a concentration of 370 mgml will be administered intravenously at a flow rate of 4 to 5 mlsec CT angiogram of the lower extremity will be taken at the arterial phase The imaging data will be exported in the Digital Imaging and Communications in Medicine DICOM format
AI-enabled segmentation of skin perforators from CT angiogram The DICOM data of the CTA will be imported to the AI workstation United Imaging Intelligence UII Shanghai China The tibia and fibula bone skin blood vessels of the lower extremities including the anterior tibial artery ATA posterior tibial artery PTA and peroneal artery PEA will be segmented with pre-set threshold range to generate the stereolithography stl models Based on the fully CNN of V-net the blood vessel branches from the PEA will be automatically segmented to build the stl model The small perforating vessels from the PEA traveling laterally crossing the posterior edge of the fibula bone to the skin surface will be marked as skin perforators The stl models of the fibula bone skin ATA PTA PEA and skin perforators will be exported from the workstation for incorporation into the VSP workflow
Virtual surgery planning
Preoperative imaging and building of models Preoperative CT scan will be imported to ProPlan CMF 21 software Materialise Leuven Belgium Volume segmentation will be performed to build the 3D virtual models of the jaws
Incorporating the skin perforators The stl models exported from the AI workstation will be imported to the ProPlan file The fibula length and skin perforator location will be measured and recorded
Virtual fibula flap harvest segmentation and dental implant insertion VSP will be performed using the methodology described in the previous publications The extent of resection will be pre-determined based on the type and extension of the pathology The osteotomies for fibula harvest and segmentation will be planned to suit the defect after guided tumour resection The location of the immediate dental implants will be marked in the fibula segments Taking the location of the skin perforators into consideration the osteotomies can be adjusted to include the take-off point of the skin perforators while avoiding placing the perforator directly in planned osteotomies or implant insertion points
Design and fabrication of surgical guides andor plates After the VSP is confirmed the surgical guides andor plates will be designed using 3-matic 130 software Materialise Leuven Belgium The location of the perforators as they cross the posterior edge of the fibula will be marked with a small sphere on the fibula harvest guide The guides will be printed with biocompatible and autoclavable resin - either MED610 Stratasys Ltd Eden Prairie MN USA or NextDent SG Vertex Dental The Netherlands
2 Surgical procedures and intraoperative recording All the surgical procedures will be conducted in the routine manner of CAS jaw reconstruction using FFF by the PA Briefly osteotomies direct dental implant insertion bone movements and bone inset will be guided by the prepared surgical templates
Intraoperative measurements will be conducted Any FP or FN perforators will be recorded Any unanticipated event that requires the modification of the fibula harvest plan will be recorded Standard perioperative management will be conducted in a routine manner
3 Postoperative evaluation Postoperative evaluation will be conducted between the surgical team led by the PA and the computer science team led by the Co-A Any intraoperative events will be discussed in a timely manner Data entry will be performed for each case right after the surgery
iii Sample size The calculation of the sample size is determined by the primary endpoint which focuses on the predictive accuracy of the AI tool in identifying the targeted skin perforators As the success rate of the conventional method in identifying a reliable correlation was 67 the aim of the AI-segmentation tool is to achieve a minimum accuracy of 80 A one-sided test for non-inferioritysuperiority is employed The sample size is calculated using the exact binomial test for a one-sample non-inferioritysuperiority clinical study The hypothesis can be unified as H0 p p0 δ versus Ha p p1 Here δ represents the intervention effect size p0 equals 80 and p is the true accuracy which is hypothesised to exceed 80 with a statistical power of at least 90 with a 5 level of significance
According to a previous report on the AI-enabled skin perforator segmentation tool the diagnostic accuracy was reported as 97 The investigators can conservatively estimate the true success rate as 95 representing an intervention effect size of 15 Based on the calculation a sample size of 44 is expected to have a 933 statistical power Assuming at least one targeted perforator for each case and a drop-out rate after signing the informed consent of 10 a total of 49 patients will be recruited to the study
iv Data processing and analysis
All the data in this protocol will be recorded using a specially designed CRF Appendix Discrepancies in the database will be traced to the original documents for resolution All records will be audited 100 for accuracy Data entry will be performed by two independent assistants using the IBM SPSS Statistics Version 25 Consistency of input data between assistants will be compared using a inbuilt function
For the demographic information data will be presented as mean values with standard deviations for continuous data and as counts with proportions for categorical data The predictive accuracy will be calculated and presented as frequency The one-sided 95 binomial proportion confidence interval will be computed and the lower confidence limit will be compared with 80 The sensitivity specificity and positive and negative predictive values will be calculated Continuous data distance of perforators identified by the AI tool and during the surgeries location of the perforators relative to the total fibula length size of the perforators time and satisfaction measurements etc will be expressed as mean values with standard deviation SD for normally distributed data and expressed as the median with interquartile range IQR or range for skewed data To evaluate the effect of various confounding variables on the precision of perforators identified by AI suitable tests such as Pearson or Spearmans correlation test t-Test or Wilcoxon Signed-rank test will be selected depending on the normality of the data Statistical significance will be set at p 005 All statistical analysis will be performed using IBM SPSS Statistics Version 25
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None