ライフサイエンスコーパス: radiograph

1 BNP would look without disease (a "healthy" radiograph).

2 ory signs and absence of infiltrate on chest radiograph).

3 738 weight-bearing anterior-posterior pelvic radiographs).

4 onary infection represented 2.0% (39 of 1964 radiographs).

5 cementoenamel junction to the root apex, on radiographs).

6 ation was performed with a supine and a Fisk radiograph.

7 Figure 1: Anteroposterior pelvic radiograph.

8 oporosis as assessed by clinical history and radiograph.

9 nd significantly outperformed portable chest radiograph.

10 tal dimension analysis on digital periapical radiographs.

11 ntly less likely to have cavitation on chest radiographs.

12 ted wrist fractures and negative findings on radiographs.

13 tudies, with 87% of patients receiving chest radiographs.

14 dule or mass, and fracture) on frontal chest radiographs.

15 for detection of COVID-19 pneumonia on chest radiographs.

16 identify the presence of calculus on digital radiographs.

17 ic validation consisting of routine clinical radiographs.

18 gnosis system for assessment of catheters on radiographs.

19 wrist fracture but with negative findings on radiographs.

20 gion of mandibular first molar on periapical radiographs.

21 of COVID-19 in humans, were visible in lung radiographs.

22 BL) were assessed using standardized digital radiographs.

23 r automated prioritization of abnormal chest radiographs.

24 nce automated binary classification of chest radiographs.

25 lation by using an independent set of 15 887 radiographs.

26 age in a curated data set of pediatric hand radiographs.

27 ere used to classify clinical LRTI and chest radiographs.

28 hich consisted of 1007 posteroanterior chest radiographs.

29 ormalized heights and hand skeletal maturity radiographs.

30 te changes in the pin position on plain film radiographs.

31 tal CBL was measured on standardized digital radiographs.

32 Ns) for detecting tuberculosis (TB) on chest radiographs.

33 BL) around all teeth was measured on digital radiographs.

34 e were no arterial placements found on chest radiographs.

35 at physical examination or on initial plain radiographs.

36 egions of the joint that are not captured by radiographs.

37 bicortical fracture of the scaphoid waist on radiographs.

38 se with and without changes evident on plain radiographs.

39 e the second most common abnormal finding on radiographs.

40 vels using standardized intraoral periapical radiographs.

41 l SWMR MIP-images, T1weighted MIP-images and radiographs.

42 and it is frequently performed after initial radiographs.

43 eumonia from non-COVID-19 pneumonia on chest radiographs.

44 Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 2

45 As some Fisk radiographs (26.7%) were also non-interpretable, that le

46 and interstitial process for portable chest radiograph (29%).

47 A retrospective review of 100 radiographs (50 [mean age 59.4 +/- 17.3, range 22-87; 30

48 both pulmonary ultrasound and portable chest radiograph (96% and 73%, respectively).

49 ty: 71% vs. 22%, specificity: 90% vs. 94% on radiographs according to New York criteria.

50 fied radiologists who evaluated supine chest radiographs according to side-separate reading scores fo

51 trained to automatically grade conventional radiographs according to the Risser classification.

52 ed collection of prospectively labeled chest radiographs achieved high diagnostic performance in the

53 8 fully anonymized institutional adult chest radiographs acquired from 2007 to 2017.

54 ed using one manufacturer (Siemens) to chest radiographs acquired using another (Philips), producing

55 to translate texture information from chest radiographs acquired using one manufacturer (Siemens) to

56 RFs, extracted from chest radiographs after the cycle-GAN's texture translation (f

57 carious changes were mounted to take digital radiographs and 3D OCT images.

58 s recorded from each subject with full mouth radiographs and clinical measurements.

59 All chest radiographs and clinical outcomes of patients, including

60 radiographic hip osteoarthritis features on radiographs and compare its performance to that of atten

61 rs and radiographic findings from periapical radiographs and Cone Beam Computed Tomographies (CBCT) w

62 A total of 14 036 clinical hand radiographs and corresponding reports were obtained from

63 ity (BMD) could be derived from CT localizer radiographs and could potentially enable opportunistic o

64 Prevention criteria for EVALI and had chest radiographs and CT images available at initial presentat

65 rface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human in

66 , including 61 women and 40 men, referred to radiographs and MR examinations by rheumatologists due t

67 The aim of the paper was to compare radiographs and MRI in assessment of active and chronic

68 s and whether there is a correlation between radiographs and MRI in their identification.

69 asked to the previous diagnosis examined the radiographs and patient data to make a diagnosis based o

70 also showed reduced pulmonary infiltrates on radiographs and reduced virus titres in bronchoalveolar

71 ogists have traditionally focused on frontal radiographs and the measurement of scoliosis curves as i

72 th an increased emphasis on standing lateral radiographs and the sagittal position of the spine.

73 The training set had 961 radiographs and the test set had 239.

74 mal images accounted for 98.0% (1925 of 1964 radiographs) and findings of pulmonary infection represe

75 nia were no fever, no consolidation on chest radiograph, and absolute neutrophil count <5 x 109/L at

76 Applicants underwent a chest radiograph, and any with results suggestive of tuberculo

77 nts had pulmonary ultrasound, portable chest radiograph, and chest CT performed within 24 hours of in

78 g for carbon monoxide, pulse oximetry, chest radiograph, and high-resolution thoracic computerized to

79 ry refill, atelectasis or pneumonia on chest radiograph, and pleural effusion.

80 period, unadjusted arterial blood gas, chest radiograph, and RBC utilization in the intervention peri

81 centives targeting arterial blood gas, chest radiograph, and RBC utilization.

82 8 fewer arterial blood gases, 73 fewer chest radiographs, and 16 fewer RBCs per 100 patients (p < 0.0

83 tisfaction outcomes, digital photographs and radiographs, and changes in probing depth, clinical atta

84 employed an oral HL (OHL) survey, full-mouth radiographs, and clinical examination.

85 Standard clinical measurements, radiographs, and intraoral photographs were taken over p

86 Gross dissection, radiographs, and magnetic resonance imaging revealed tha

87 Here, clinical records, radiographs, and synovial fluid samples from 30 dogs tha

88 Chest radiograph approximated accurate catheter tip position i

89 Radiographs are the clinical first line imaging modality

90 Radiographs are used to measure the most morphometric pa

91 Two radiologists classified each radiograph as adequate or inadequate.

92 erienced radiologists categorized each chest radiograph as characteristic, nonspecific, or negative i

93 yzed the free-text report to prioritize each radiograph as critical, urgent, nonurgent, or normal.

94 enced radiologists who identified fake chest radiographs as belonging to a target manufacturer class.

95 s, ML classifiers categorized the fake chest radiographs as belonging to the target manufacturer rath

96 rd, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the

97 d-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-o

98 Using cycle-GAN-generated chest radiographs as inputs, ML classifiers categorized the fa

99 set was used to train CNNs to classify chest radiographs as normal or abnormal before evaluation on a

100 c performance in the classification of chest radiographs as normal or abnormal; this function may be

101 sion analysis and Bland-Altmann Plots, using radiographs as reference standard.

102 ty; and percentage of opacification on chest radiograph at drain removal and at 30, 90, and 180 days.

103 Two localizer radiographs at different combinations of tube voltages w

104 are accurate and precise using two localizer radiographs at different tube voltages from energy-integ

105 osition of catheters must be assessed on all radiographs because serious complications can arise if c

106 Repeating radiographs before each subsequent IACS injection remain

107 rall rate of hip fractures not identified on radiographs but that require surgery (ie, surgical hip f

108 in the assessment of bone maturity based on radiographs.(C) RSNA, 2020.

109 Chest radiograph categorization was compared against RT-PCR re

110 included 79 185 matched A and B Reader chest radiograph classifications.

111 The initial chest radiographs, clinical variables, and outcomes, including

112 nce of vertebral fractures as assessed using radiographs collected at baseline, 6 and 12 months, year

113 king into account a distortion rate for each radiograph compared with original implant measurements.

114 d with increased severity on admission chest radiographs compared with White or non-Hispanic patients

115 Side-by-side radiograph comparisons were used to define progression a

116 (PVR) obtained in routine follow-up digital radiographs could be used for such assessment.

117 atient was examined with ultrasound, sternal radiographs, CT and MRI.

118 Conventional imaging, including radiographs, CT, MRI, and bone scintigraphy, are recogni

119 For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0

120 A-LRI and overall rates of visits with chest radiograph (CXR) examination in the pediatrics emergency

121 CAAP, NA-LRIs, and overall visits with chest radiograph (CXR) examination rates in the pediatric emer

122 microbiologically confirmed cases and chest radiograph (CXR)-positive cases compared to controls.

123 of the chest (98.0% and 100%) and for chest radiograph (CXR; 57.6% and 100%).

124 Chest radiographs (CXRs) are a valuable diagnostic tool in epi

125 Chest radiographs (CXRs) are frequently used to assess pneumon

126 vailable National Institutes of Health chest radiograph dataset comprising 112 120 chest radiographic

127 Lateral chest radiograph demonstrated lytic destruction of the xiphist

128 Radiographs do not allow early inflammatory lesions indi

129 g was applied to DS1 to account for positive radiograph enrichment and estimate population-level perf

130 Of the 100 portable chest radiographs evaluated by three reviewers, two reviewers

131 nical decision support tool for supine chest radiograph examinations in the clinical routine with hig

132 nslation, measured from static, end of range radiographs exceeds 3 mm.

133 criteria with bilateral infiltrates on chest radiograph experience a more intense early inflammatory

134 visualizing neural network learning of chest radiograph features in congestive heart failure (CHF).

135 adiologists were blinded to the supine chest radiograph findings during CT interpretation.

136 dels for detecting clinically relevant chest radiograph findings were developed for this study by usi

137 t of pulmonary ultrasound and portable chest radiograph findings with correlating lobe ("lobe-specifi

138 ulin skin test, syphilis serology, and chest radiograph) followed by more complex investigations acco

139 lly relevant complications detected on chest radiographs following ultrasound-guided right internal j

140 lly relevant complications detected on chest radiographs following ultrasound-guided right internal j

141 ween pulmonary ultrasound and portable chest radiograph for interstitial findings (86% vs 29%, respec

142 d cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically vali

143 the distal fibula are rare in children with radiograph fracture-negative lateral ankle injuries.

144 ve study, 22 960 de-identified frontal chest radiographs from 11 153 patients (average age, 60.2 year

145 Hip and pelvic radiographs from 1118 studies were reviewed, and 3026 hi

146 A total of 214 trauma hand radiographs from Hasbro Children's Hospital were used as

147 tecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon.

148 on weight-bearing anterior-posterior pelvic radiographs from participants in the Osteoarthritis Init

149 Radiographs from the Osteoarthritis Initiative staged by

150 ent with necrotising enterocolitis and whose radiographs fulfilled criteria for Bell's stage 2 or 3 n

151 Most supine radiographs (>75%) were non-interpretable and were exclu

152 ny WHO danger sign or consolidation on chest radiograph had an NPV of 96.8% for adverse pneumonia out

153 Background Disease severity on chest radiographs has been associated with higher risk of dise

154 omated deep-learning approach based on chest radiograph images may identify more smokers at high risk

155 udy visit in participants for whom evaluable radiograph images were available at baseline and at leas

156 aortic valve location on plain supine chest radiograph images, which can be used to evaluate intraca

157 ne the aortic valve location on supine chest radiograph images.

158 A plain radiograph in frog leg position showed a widening of the

159 ociated with alveolar consolidation on chest radiograph in nonconfirmed cases, and with high (>6.9 lo

160 ld be easily monitored on plain supine chest radiograph in the ICU.

161 and Methods A total of 103 489 frontal chest radiographs in 46 712 patients acquired from January 1,

162 sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Ne

163 with standard MR-sequences, coronal SWMR and radiographs in anteroposterior pelvic view.

164 nced high-tech imaging, the utility of plain radiographs in conditions of the bone is increasingly be

165 del for risk of OA progression by using knee radiographs in patients who underwent total knee replace

166 was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumo

167 on-interpretable, that left 44 interpretable radiographs in the study.

168 (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation whi

169 and evaluate deep learning models for chest radiograph interpretation by using radiologist-adjudicat

170 Conclusion Chest radiograph interpretation skill increased with experienc

171 pose To investigate the development of chest radiograph interpretation skill through medical training

172 pects such as antibiotic pretreatment, chest radiograph interpretation, utility of induced sputum in

173 that can reliably predict disease, while 2D radiographs interpreted by human observers are still the

174 comparing efficacy of CBCT versus intraoral radiographs (IRs).

175 can be substantial delays between the time a radiograph is obtained and when it is interpreted by a r

176 Plain radiograph is the initial modality used to evaluate pati

177 rning model trained on pediatric trauma hand radiographs is on par with automated and manual GP-based

178 tween adequate and inadequate lateral airway radiographs is reported.

179 viewers for detecting abnormalities on chest radiographs (kappa = 0.99; 95% confidence interval: 0.97

180 eatures of congestive heart failure on chest radiographs learned by neural networks can be identified

181 this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained

182 ods are used - dental radiographs, panoramic radiographs, magnetic resonance imaging with diffusion-w

183 logists had difficulty recognizing the chest radiographs' manufacturer.

184 Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years +/- 16 [standard deviati

185 D-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years +/- 18; 1578 men) with n

186 severity of lung disease on admission chest radiographs, measured by using the modified Radiographic

187 e (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection cont

188 ms were fever (49%) and cough (39%); initial radiographs most commonly showed multifocal or bilateral

189 sted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of

190 1.27] per cycle threshold [CT]), and a chest radiograph not suggestive of active tuberculosis (aOR, 0

191 previous tuberculosis, high CT, and a chest radiograph not suggestive of active tuberculosis.

192 Prior chest radiographs (not shown) were normal, and angiography per

193 d to lower lung zone distribution on a chest radiograph obtained in the setting of pandemic COVID-19

194 tegrating detector CT and a single localizer radiograph obtained with photon-counting detector CT.

195 for evaluation of findings on thoracic spine radiographs obtained at a peripheral hospital.

196 a retrospective study, 216 431 frontal chest radiographs obtained between 1998 and 2012 were procured

197 on 15 129 frontal view pediatric trauma hand radiographs obtained between December 14, 2009, and May

198 However, due to the large number of radiographs obtained each day, there can be substantial

199 A total of 1200 lateral airway radiographs obtained in emergency department patients be

200 Radiographs obtained longitudinally from Lohmann Brown l

201 ision of BMD measurement using two localizer radiographs obtained with energy-integrating detector CT

202 A frontal radiograph of the pelvis taken six months before showed

203 r distance was measured on the mammogram and radiograph of the specimen, and reflector depth was meas

204 ds, which their curves were derived from the radiographs of 129 sagittal spinal curves of adolescents

205 eep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy

206 Radiographs of alginate hydrogel with PRP-treated bone d

207 Radiographs of both hands were obtained.

208 roved-study, a total of 1830 posteroanterior radiographs of patients with AIS (age range, 10-18 years

209 Supine anterior-posterior chest radiographs of patients with an aortic valve prosthesis

210 Digital radiographs of teeth taken before extraction were modifi

211 Radiographs of the hands and knees were obtained.

212 Five of 110 radiographs of the specimen (4.5%; 95% CI: 1.7%, 10.4%)

213 Radiographs of the specimen and pathologic analysis help

214 The PVR from radiographs of thirty children with ceramic bone substit

215 Plain radiographs often play a pivotal role in diagnosing meta

216 s with wrist trauma and negative findings on radiographs often undergo additional MRI examinations to

217 Postoperative chest radiographs on postoperative days 1, 3, and 8 (Fig 1a-1c

218 automated real-time triaging of adult chest radiographs on the basis of the urgency of imaging appea

219 crobial use (P = 0.032), and number of chest radiographs (P = 0.005), when controlling for potential

220 following imaging methods are used - dental radiographs, panoramic radiographs, magnetic resonance i

221 For the non-COVID-19 chest radiographs, patients with pneumonia who underwent chest

222 For the chest radiographs positive for COVID-19, patients with reverse

223 The system's performance in radiograph prioritization was tested in a simulation by

224 eved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and

225 0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia.

226 come a standard of care, postinsertion chest radiograph remains the gold standard to confirm central

227 tation of lung opacities in ICU supine chest radiographs remains challenging.

228 thicknesses were extracted directly from the radiographs, representing a greatly enhanced scope of da

229 both pulmonary ultrasound and portable chest radiograph respectively (right lung: 92.5% vs 65.7%; p <

230 near-normal MRI of the spine, in whom plain radiographs revealed subtle findings and aided in making

231 Analysis of specific radiographs revealed that the model was heavily influenc

232 ween pulmonary ultrasound and portable chest radiograph (right: 99% vs 87%; p = 0.009 and left: 99% v

233 Chest radiograph ruled out pneumothorax in 137 of 137 patients

234 Among patients who were admitted, a chest radiograph score of 3 or more was an independent predict

235 ationship between clinical parameters, chest radiograph scores, and patient outcomes.

236 ll foci suspicious of lytic lesions on skull radiographs, seen as arachnoid granulations fovea in CT.

237 hospital admission (n = 145, 43%) were chest radiograph severity score of 2 or more (odds ratio, 6.2;

238 The model trained on the chest radiograph severity score produced the following areas u

239 senting to the emergency department, a chest radiograph severity score was predictive of risk for hos

240 Cs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for

241 e To analyze the prognostic value of a chest radiograph severity scoring system for younger (nonelder

242 uted tomography of L4 vertebrae and skeletal radiographs showed delayed skeletal development and sugg

243 Chest radiographs showed mild pulmonary edema with a small rig

244 cycle-GAN's texture translation (fake chest radiographs), showed decreased intermanufacturer RF vari

245 ortion of implant, and subsequent periapical radiographs taken demonstrated a radiolucent lesion.

246 ll, many had residual abnormalities on chest radiographs (ten [67%] of 15) and pulmonary function tes

247 igher severity of disease on admission chest radiographs than White or non-Hispanic patients, and inc

248 ing AI algorithm to detect COVID-19 on chest radiographs, that was trained and tested on a large clin

249 On radiographs (the standard of reference), 27 patients had

250 the senior radiologist (V.M.C.) reviewed the radiographs, the patient was called back for assessment

251 pare susceptibility weighted MRI (SWMR) with radiographs to evaluate hip morphology.

252 deep CNNs was then trained by using labeled radiographs to predict the clinical priority from radiol

253 a convolutional neural network (trauma hand radiograph-trained deep learning bone age assessment met

254 Dental age was assessed from panoramic radiographs using the Demirjian's method.

255 An infiltrate on chest radiograph was considered the reference standard for the

256 Each patient's ED chest radiograph was divided into six zones and examined for o

257 y-integrating detector CT, and one localizer radiograph was obtained with photon-counting detector CT

258 Each radiograph was preprocessed and cropped to include the e

259 tection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent

260 a from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood

261 months following injury, through both CT and radiographs, was 24.3 mSv.

262 rcentage IBD depth reduction, assessed using radiographs, was evaluated at baseline and postoperative

263 is and Kellgren-Lawrence grade I-II on plain radiograph were included for MRI knee.

264 One hundred external test radiographs were also obtained from a different hospital

265 Current and previous spine radiographs were also reviewed.

266 Standardized radiographs were also taken to evaluate linear bone gain

267 Angles and time for 30 random radiographs were compared by using repeated-measures ana

268 Migrants whose chest radiographs were compatible with active tuberculosis but

269 Results Normal chest radiographs were detected by our AI system with a sensit

270 Periapical radiographs were evaluated immediately after implant pla

271 ized procedure notes and postprocedure chest radiographs were extracted and individually reviewed to

272 Radiographs were independently analyzed by six readers a

273 Chest radiographs were interpreted according to the Internatio

274 Skull and chest radiographs were obtained (Figs 1, 2), and the patient u

275 Chest radiographs were obtained after implantation.

276 Posteroanterior and lateral chest radiographs were obtained in the emergency department.

277 A total of 1964 chest radiographs were obtained, of which normal images accoun

278 At this time, chest radiographs were obtained.

279 Chest radiographs were read according to a WHO standard.

280 Clinical parameters and periapical radiographs were registered on the day of implant placem

281 Medical records and chest radiographs were reviewed for the main tertiary hospital

282 Periapical radiographs were taken using the long-cone technique bef

283 Radiation charts and simulation radiographs were used to estimate in-field heart volume

284 Periapical radiographs were used to evaluate changes in crestal bon

285 The radiographs were used to train and test the DL classifie

286 and lung ultrasound is noninferior to chest radiograph when used to accurately assess central venous

287 onary ultrasound protocol and portable chest radiograph with chest CT for localization of pathology t

288 tegrating detector CT and a single localizer radiograph with different energy thresholds from photon-

289 The model was used to visualize how a radiograph with high estimated BNP would look without di

290 which included all conventional screen-film radiographs with a classification by at least one A Read

291 = 0.38; T1weighted: R(2) = 0.18) compared to radiographs with a higher accuracy than conventional MR

292 , detected coronavirus disease 2019 on chest radiographs with a performance similar to that of experi

293 proficient in differentiating between chest radiographs with and without symptoms of pneumonia but h

294 25 mm of reproducible sensor displacement on radiographs with as little as 100 N of axial compressive

295 sing another (Philips), producing fake chest radiographs with different textures.

296 sion A Readers classified substantially more radiographs with evidence of pneumoconiosis and classifi

297 hold the potential to assist in prioritizing radiographs with potentially malpositioned catheters for

298 ning bone age assessment model based on hand radiographs with that of expert radiologists and that of

299 Automated real-time triaging of adult chest radiographs with use of an artificial intelligence syste

300 eveloped to detect COVID-19 on frontal chest radiographs, with reverse-transcription polymerase chain