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1 ere performed to determine intraobserver and interobserver variability.
2 -to-muscle contrast and demonstrated minimal interobserver variability.
3 opulation characteristics, CT technique, and interobserver variability.
4 pa analysis was also performed to assess for interobserver variability.
5 e and mass with good accuracy and acceptable interobserver variability.
6 art, and we calculated the intraobserver and interobserver variability.
7 icient of variation was calculated to assess interobserver variability.
8 images had the highest specificity and least interobserver variability.
9 MR images and is an important contributor to interobserver variability.
10 RY are robust radiomics tools with excellent interobserver variability.
11 >=7) but is limited by reader experience and interobserver variability.
12 icipants with CRLM demonstrated considerable interobserver variability.
13 oper biopsy orientation, and it suffers from interobserver variability.
14 plex or have not been assessed for intra- or interobserver variability.
15 underestimation of flow values and increased interobserver variability.
16 DCIS is challenging due to undersampling and interobserver variability.
17 ility of pathologists with IBD expertise and interobserver variability.
18 as associated with significant reductions in interobserver variability.
19 e diagnosis of LGD is limited by substantial interobserver variability.
20  is laborious and may be prone to intra- and interobserver variability.
21 iquantitative and can have intraobserver and interobserver variability.
22 however, its assessment is complex with high interobserver variability.
23 is approach is time-consuming and subject to interobserver variability.
24 mode echogenicity are negatively affected by interobserver variability.
25 PS has started efforts aimed at reducing the interobserver variability.
26 prone to error due to tumor heterogeneity or interobserver variability.
27 st Gleason pattern segmentation despite high interobserver variability.
28 ch optimization method was evaluated through interobserver variability.
29 hology, which is associated with substantial interobserver variability.
30 between surgeon and radiologist may decrease interobserver variability.
31 to assess the deep learning model as well as interobserver variability.
32 p vascular network may be subject to greater interobserver variability.
33  access to both SBR and CPR data to minimize interobserver variability.
34 -Altman plots were used to assess intra- and interobserver variability.
35 orrelation coefficient was used to determine interobserver variability.
36 ilcoxon signed-rank test were used to assess interobserver variability.
37  scans from patients with nAMD is subject to interobserver variability.
38  reprocessed for determination of intra- and interobserver variability.
39 used in rheumatoid arthritis (RA), have high interobserver variability.
40 nature of the procedure, sampling error, and interobserver variability.
41 r-intensive analyses and potential intra- or interobserver variability.
42  there has been little attempt to quantitate interobserver variability.
43 due to PE, but with low sensitivity and high interobserver variability.
44  thickness were assessed, as were intra- and interobserver variability.
45 were used to evaluate both intraobserver and interobserver variability.
46 sible for significantly increased intra- and interobserver variabilities.
47 Additionally, the RT3D technique reduced the interobserver variability (37% to 7%) and intraobserver
48 e index (diagnostic accuracy range, 50%-87%; interobserver variability, +/-7%).
49                                To assess the interobserver variability, a Cohen kappa analysis was us
50 out contrast injection for intraobserver and interobserver variabilities (all p < 0.001).
51 ssification with a high accuracy and without interobserver variability, along with the molecular reso
52     Practice Advice 2: Given the significant interobserver variability among pathologists, the diagno
53                                   Intra- and interobserver variability analyses showed high agreement
54                                  By avoiding interobserver variability and accelerating computation,
55 mance to conventional assessment and reduced interobserver variability and assessment time.
56 ented in practice, AI-based VSS could reduce interobserver variability and could standardize treatmen
57  assessment, quantitative assessment has low interobserver variability and could yield a tumor size c
58 to routine practice because it is limited by interobserver variability and generally only meets accep
59                                     However, interobserver variability and image quality influence ob
60 ved a more guarded reception lately owing to interobserver variability and lack of standardized proto
61 stological features, generating considerable interobserver variability and limited diagnostic reprodu
62  compared with T2-weighted imaging, reducing interobserver variability and measurement error.
63 ctional MR examination significantly reduces interobserver variability and offers reliable and reprod
64                                              Interobserver variability and practice guidelines remain
65                                              Interobserver variability and specialized experience are
66 n tumour histology) resulted in considerable interobserver variability and substantial variation in p
67                                              Interobserver variability and the correlation between au
68 tial but also challenging due to significant interobserver variability and the time consumed in manua
69 1.6% for intraobserver variability, 4.0% for interobserver variability, and 10.3% for scan-rescan var
70 .6% for intraobserver variability, 10.7% for interobserver variability, and 19.8% for scan-rescan var
71 0.7% for intraobserver variability, 1.5% for interobserver variability, and 8.1% for scan-rescan vari
72 le segmentation is labor intensive, prone to interobserver variability, and impractical for large-sca
73 to improve endoluminal visualization, reduce interobserver variability, and improve patient acceptanc
74 e heterogeneity quantification, with reduced interobserver variability, and independent prognostic va
75 and stages steatosis accurately with limited interobserver variability, and performance is not hamper
76 ompliance is more often identified, has less interobserver variability, and poses less risk to the pa
77 er biopsy is associated with sampling error, interobserver variability, and potential complications.
78 n interclass correlation were used to define interobserver variability, and receiver operating charac
79 ppropriate testing, improve accuracy, reduce interobserver variability, and reduce diagnostic and rep
80 n PET measures (22%-44%) was attributable to interobserver variability as measured by the reader stud
81                    The addition also reduced interobserver variability (Az = 0.86 vs Az = 0.75).
82             To study possible differences in interobserver variability between the 3 applied PSMA rad
83 ncer patients were analyzed to determine the interobserver variability between the automated BSIs and
84 ations still exist including sampling error, interobserver variability, bleeding, arteriovenous fistu
85       3D-Gd-MRA revealed a slightly improved interobserver variability but incorrectly graded 6 of 34
86  evaluation of renal artery stenosis with an interobserver variability comparable with that of conven
87      LV-METRIC had reduced intraobserver and interobserver variability compared with other methods.
88                            Owing to the high interobserver variability, CT scan was not associated wi
89  g +/- 9, kappa = 0.49 [P < .0001]) and less interobserver variability (difference, 5.4 g +/- 18, kap
90                                  Conclusion: Interobserver variability differs among the 3 clinically
91 cinoma is of major importance; however, high interobserver variability exists.
92                                              Interobserver variability expressed as 1 SD was 3.6 mm f
93  (F = 6.9, P = 0.011; trained observers) and interobserver variability (F = 33.7, P = 0.004; group of
94 ed with TTE, CMR has lower intraobserver and interobserver variabilities for RVol(AR), suggesting CMR
95 pectively compare diagnostic performance and interobserver variability for computed tomography (CT) a
96                                              Interobserver variability for conventional angiograms wa
97 roach can provide a significant reduction in interobserver variability for DCE MR imaging measurement
98  considered clinically insignificant because interobserver variability for echocardiographic measurem
99                                              Interobserver variability for individual CT findings was
100                                              Interobserver variability for interpretation of the lesi
101                             However, data on interobserver variability for pancreatoduodenectomy-spec
102  (100% versus 47%; P<0.0001) and with better interobserver variability for RT-ungated (coefficient of
103                            Intraobserver and interobserver variability for score assessment were 6% a
104                                         Good interobserver variability for the definitions of surgica
105                                              Interobserver variability for the degree of renal artery
106                        DSA had a substantial interobserver variability for the grading of stenosis (m
107                                          The interobserver variability for the ISGPS-defined complica
108               kappa Values for assessment of interobserver variability for the T2, single-voxel, mult
109                             Herein we report interobserver variability for two validated radiomic too
110 t the two ROIs demonstrated good to moderate interobserver variability (for the two ROIs, 0.46 and 0.
111 ese challenges, however, they are subject to interobserver variability if semi-automated segmentation
112     This finding may be associated with high interobserver variability in Apgar scoring, reduced vita
113 ial for improving specificity and decreasing interobserver variability in biopsy recommendations.
114  causality, but there was still considerable interobserver variability in both.
115 eneous histological patterns and substantial interobserver variability in classification.
116         This AI system overcomes substantial interobserver variability in expert predictions, perform
117  its clinical application remains limited by interobserver variability in grading and quantification,
118             The uncertainty is compounded by interobserver variability in histologic diagnosis.
119                              Analyses of the interobserver variability in hue scaling revealed multip
120 ot quite as good, and there is slightly more interobserver variability in interpretation.
121  a significant difference, there was greater interobserver variability in lesion descriptions among r
122                        There was significant interobserver variability in Pflex, with a maximum diffe
123 ime needed to complete this task, as well as interobserver variability in radiologist predictions.Key
124                                              Interobserver variability in reporting between a senior
125     A change of >32 mum was likely to exceed interobserver variability in SFCT.
126  imaging can have may be in the reduction of interobserver variability in target volume delineation a
127  of best practice guidelines results in high interobserver variability in TC assessments.
128 acy for less experienced readers and reduces interobserver variability in the diagnosis of ECE of pro
129                         There is significant interobserver variability in the diagnosis of LGD even a
130 as evaluated in the 2 trained observers, and interobserver variability in the group of 15 observers.
131 chnique also minimized right-left kidney and interobserver variability in the measurement of EF.
132                                        Large interobserver variability in the measurement of vascular
133 s investigations have identified significant interobserver variability in the measurements of central
134                          Purpose To evaluate interobserver variability in the morphologic tumor respo
135 n that of physicians, who showed significant interobserver variability in their assessment.
136 eatures, and radiology residents had greater interobserver variability in their selection of five of
137  doses, reducing the toxicity issues and the interobserver variability in tumor detection.
138                                              Interobserver variability, interobserver correlation, an
139 FI vascularization flow index for intra- and interobserver variability; intraobserver values were 0.9
140  compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to
141                                              Interobserver variability is presented with Gwet AC-1 me
142                                              Interobserver variability (kappa statistic or intraclass
143 toxylin and eosin-stained slides is prone to interobserver variability, leading to inconsistent clini
144                 The MRA measurements had low interobserver variability (&lt; or =5%) and good correlatio
145                                              Interobserver variability may be reduced in the future b
146 A and PC-flow revealed the best (P = 0.0003) interobserver variability (median kappa = 0.75) and almo
147 aobserver variations were small, with a mean interobserver variability of -0.1 g +/- 2.3 and a mean i
148 ty to the radiologists with 0.74 mm than the interobserver variability of 0.77 mm and generalised to
149 Our purpose was to determine and compare the interobserver variability of 3 clinically frequently use
150 d to evaluate the diagnostic performance and interobserver variability of CO-RADS (COVID-19 Reporting
151 been reported evaluating the performance and interobserver variability of computerized tomographic co
152 rdance with current guidelines to assess the interobserver variability of FCT measurement by intracla
153 es and calcification contributed to the high interobserver variability of FCT measurement.
154                                  The overall interobserver variability of K(trans) with manual ROI pl
155 s to determine preliminary intraobserver and interobserver variability of measurements in a subset of
156  normal values, and determine the intra- and interobserver variability of measurements.
157                                          The interobserver variability of MRI and the relative import
158                             Knowledge of the interobserver variability of quantitative parameters is
159 idated by comparing its accuracy against the interobserver variability of six trained graders from th
160 orrections that in turn resulted in a higher interobserver variability of SUVmean (CCCs for follow-up
161         These results were compared with the interobserver variability of the same radiologists obtai
162                 Our results show substantial interobserver variability, particularly for overall diag
163 SPECT/CT demonstrated both a high intra- and interobserver variability (R(2) = 0.997) and an accuracy
164                           Overall intra- and interobserver variability rates were similar; in clinica
165 F-PSMA-1007 showed a significantly increased interobserver variability regarding bone metastases, com
166 F-PSMA-1007 showed a significantly increased interobserver variability regarding overall agreement an
167 ce and positive correlation, but significant interobserver variability remains.
168 e but have poor diagnostic accuracy and wide interobserver variability that limit their reproducibili
169 al studies are required to further establish interobserver variability, to assess intraobserver varia
170                     Our study showed minimal interobserver variability using CAM based quantification
171 vity determination, assessment of intra- and interobserver variability, validation of data from qPSMA
172 ment of regional wall motion, and intra- and interobserver variability values are low.
173 e interstudy reproducibility, and intra- and interobserver variability values were analyzed.
174 of myocardial velocity with small intra- and interobserver variability values.
175                                         High interobserver variability warrants further investigation
176                     The mean kappa value for interobserver variability was 0.62 (95% confidence inter
177                                              Interobserver variability was analyzed by calculating in
178                                              Interobserver variability was analyzed by using three di
179                                              Interobserver variability was analyzed by using weighed
180                                              Interobserver variability was analyzed.
181                                              Interobserver variability was assessed by placing cases
182                                              Interobserver variability was assessed by using the Pear
183                                              Interobserver variability was assessed for the grading o
184                                              Interobserver variability was assessed in 10 patients.
185                                   Intra- and interobserver variability was assessed in a subset of 18
186                                              Interobserver variability was assessed with the Cohen ka
187                                 Interlot and interobserver variability was assessed.
188 reast Imaging Reporting and Data System, and interobserver variability was calculated with the Cohen
189         Descriptive statistics were used and interobserver variability was calculated.
190                                              Interobserver variability was calculated.
191                                          The interobserver variability was compared to the variabilit
192                                              Interobserver variability was determined (kappa analysis
193 d for assessment by the senior observer, and interobserver variability was determined.
194                                              Interobserver variability was evaluated using a sample o
195 by expert readers (r = 0.96; p < 0.001), but interobserver variability was greater (3.4 +/- 2.9% vs.
196                                              Interobserver variability was high for CT colonography w
197  EF than for manual EF or manual LS, whereas interobserver variability was higher for both visual and
198                                  Significant interobserver variability was identified during these as
199                                              Interobserver variability was negligible.
200                                              Interobserver variability was not explained by positive
201                                              Interobserver variability was not statistically signific
202                                  Significant interobserver variability was observed (P < .001).
203                                              Interobserver variability was only fair (kappa = 0.54) f
204                                              Interobserver variability was reported using multirater
205                            Intraobserver and interobserver variability was small, with intraclass cor
206                                   Intra- and interobserver variability was tested by using intraclass
207  sum test and two-sample Student t test, and interobserver variability was tested with kappa coeffici
208                                              Interobserver variability was tested with the kappa coef
209 ct patient outcomes and overcome substantial interobserver variability, we developed an unsupervised
210                            Intraobserver and interobserver variabilities were determined.
211                            Intraobserver and interobserver variabilities were excellent (4+/-4% and 4
212                            Intraobserver and interobserver variabilities were similar.
213 an square percent error (accuracy), bias and interobserver variability were 0.992, 11.9 g, 4.8%, -4.9
214                                   Intra- and interobserver variability were analyzed in image categor
215                           Accuracy, bias and interobserver variability were calculated.
216                            Intermodality and interobserver variability were measured using the intrac
217   Whole-lesion measurement showed the lowest interobserver variability with both measurement methods
218  manual segmentation, with errors similar to interobserver variability with manual segmentation.
219                                              Interobserver variability yielded excellent agreement fo

 
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