BACKGROUND Femoral trochlear dysplasia(FTD)is an important risk factor for patellar instability.Dejour classification is widely used at present and relies on standard lateral X-rays,which are not common in clinical wo...BACKGROUND Femoral trochlear dysplasia(FTD)is an important risk factor for patellar instability.Dejour classification is widely used at present and relies on standard lateral X-rays,which are not common in clinical work.Therefore,magnetic resonance imaging(MRI)has become the first choice for the diagnosis of FTD.However,manually measuring is tedious,time-consuming,and easily produces great variability.AIM To use artificial intelligence(AI)to assist diagnosing FTD on MRI images and to evaluate its reliability.METHODS We searched 464 knee MRI cases between January 2019 and December 2020,including FTD(n=202)and normal trochlea(n=252).This paper adopts the heatmap regression method to detect the key points network.For the final evaluation,several metrics(accuracy,sensitivity,specificity,etc.)were calculated.RESULTS The accuracy,sensitivity,specificity,positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96.All values were superior to junior doctors and intermediate doctors,similar to senior doctors.However,diagnostic time was much lower than that of junior doctors and intermediate doctors.CONCLUSION The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.展开更多
Patellofemoral instability(PI)is the disruption of the patella’s relationship with the trochlear groove as a result of abnormal movement of the patella.To identify the presence of PI,conventional radiographs(anteropo...Patellofemoral instability(PI)is the disruption of the patella’s relationship with the trochlear groove as a result of abnormal movement of the patella.To identify the presence of PI,conventional radiographs(anteroposterior,lateral,and axial or skyline views),magnetic resonance imaging,and computed tomography are used.In this study,we examined four main instability factors:Trochlear dysplasia,patella alta,tibial tuberosity–trochlear groove distance,and patellar tilt.We also briefly review some of the other assessment methods used in the quantitative and qualitative assessment of the patellofemoral joint,such as patellar size and shape,lateral trochlear inclination,trochlear depth,trochlear angle,and sulcus angle,in cases of PI.In addition,we reviewed the evaluation of coronal alignment,femoral anteversion,and tibial torsion.Possible causes of error that can be made when evaluating these factors are examined.PI is a multi-factorial problem.Many problems affecting bone structure and muscles morphologically and functionally can cause this condition.It is necessary to understand normal anatomy and biomechanics to make more accurate radiological measurements and to identify causes.Knowing the possible causes of measurement errors that may occur during radiological measurements and avoiding these pitfalls can provide a more reliable road map for treatment.This determines whether the disease will be treated medically and with rehabilitation or surgery without causing further complications.展开更多
文摘BACKGROUND Femoral trochlear dysplasia(FTD)is an important risk factor for patellar instability.Dejour classification is widely used at present and relies on standard lateral X-rays,which are not common in clinical work.Therefore,magnetic resonance imaging(MRI)has become the first choice for the diagnosis of FTD.However,manually measuring is tedious,time-consuming,and easily produces great variability.AIM To use artificial intelligence(AI)to assist diagnosing FTD on MRI images and to evaluate its reliability.METHODS We searched 464 knee MRI cases between January 2019 and December 2020,including FTD(n=202)and normal trochlea(n=252).This paper adopts the heatmap regression method to detect the key points network.For the final evaluation,several metrics(accuracy,sensitivity,specificity,etc.)were calculated.RESULTS The accuracy,sensitivity,specificity,positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96.All values were superior to junior doctors and intermediate doctors,similar to senior doctors.However,diagnostic time was much lower than that of junior doctors and intermediate doctors.CONCLUSION The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.
文摘Patellofemoral instability(PI)is the disruption of the patella’s relationship with the trochlear groove as a result of abnormal movement of the patella.To identify the presence of PI,conventional radiographs(anteroposterior,lateral,and axial or skyline views),magnetic resonance imaging,and computed tomography are used.In this study,we examined four main instability factors:Trochlear dysplasia,patella alta,tibial tuberosity–trochlear groove distance,and patellar tilt.We also briefly review some of the other assessment methods used in the quantitative and qualitative assessment of the patellofemoral joint,such as patellar size and shape,lateral trochlear inclination,trochlear depth,trochlear angle,and sulcus angle,in cases of PI.In addition,we reviewed the evaluation of coronal alignment,femoral anteversion,and tibial torsion.Possible causes of error that can be made when evaluating these factors are examined.PI is a multi-factorial problem.Many problems affecting bone structure and muscles morphologically and functionally can cause this condition.It is necessary to understand normal anatomy and biomechanics to make more accurate radiological measurements and to identify causes.Knowing the possible causes of measurement errors that may occur during radiological measurements and avoiding these pitfalls can provide a more reliable road map for treatment.This determines whether the disease will be treated medically and with rehabilitation or surgery without causing further complications.
