Reducing Diagnostic Errors in Musculoskeletal Trauma by Reviewing Non-Admission Orthopaedic Referrals in the Next-Day Trauma Meeting

INTRODUCTION Diagnostic errors in orthopaedics are usually caused by missing a fracture or misreading radiographs. The aim of this study was to document the pick-up rate of the wrong diagnoses by reviewing X-rays and casualty notes in the next-day trauma meeting. PATIENTS AND METHODS The casualty notes and radiographs of 503 patients were prospectively reviewed in the daily trauma meeting between August 2002 and December 2002 in a district general hospital. The relevant data were collected and analysed by a single assessor. RESULTS The false positive rate for making an orthopaedic diagnosis was 12.6% (i.e.) diagnosing a fracture, when none existed). The false negative (missing) rate was 4%, while 2.4% incidental findings were missed, or at least not documented, after reading the X-rays. There were 7.8% wrong diagnoses made. The majority of the patients were seen by the senior house officers. CONCLUSIONS The medicolegal significance of false negative diagnosis is obviously greater. In a busy emergency department, where a large number of patients are seen, there is a greater risk. This study shows the importance in a small-to-medium sized accident and emergency unit as well, where there is no senior cover available out-of-hours for final radiological interpretation. A morning trauma meeting which covers reviewing admitted patients as well as non-admission orthopaedic referrals has an effective risk management solution to early detection of missed and wrong diagnoses.

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Computed Tomography in Partial Carpal Arthrodesis

Journal of Hand Surgery (European Volume) ◽

10.1016/s0266-7681(97)80367-1 ◽

1997 ◽

Vol 22 (5) ◽

pp. 653-655

Author(s):

J. M. SOLER-MINOVES ◽

J. GONZALEZ-USTES ◽

R. PÉREZ ◽

M. GIFREU ◽

A. M. GALLART

Keyword(s):

Computed Tomography ◽

False Positive ◽

False Positive Rate ◽

False Negative ◽

Plain Radiography ◽

X Rays ◽

Positive Rate

We carried out X-rays and computed tomography in 59 wrists in patients who had previous surgical intercarpal fusions. 1.2 mm thick axial images were obtained perpendicular to the axis of the joint. CT showed whether or not the carpal fusions were united. Compared with CT, plain radiography yielded a 25% false negative and 6% false positive rate. We conclude that CT is more useful than plain X-rays for evaluating partial carpal arthrodesis.

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Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

Journal of X-Ray Science and Technology ◽

10.3233/xst-211047 ◽

2021 ◽

pp. 1-21

Author(s):

Asma Naseer ◽

Maria Tamoor ◽

Arifah Azhar

Keyword(s):

Neural Network ◽

Short Term Memory ◽

False Positive Rate ◽

False Negative ◽

True Positive Rate ◽

X Rays ◽

Infected People ◽

Computer Aided ◽

High True Positive Rate ◽

Positive Rate

Background: Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a variety of symptoms such as fatigue, fever, tastelessness, dry cough, etc. Some other symptoms may also be manifested by radiographic visual identification. Therefore, Chest X-Rays (CXR) play a key role in the diagnosis of COVID-19. Methods: In this study, we use Chest X-Rays images to develop a computer-aided diagnosis (CAD) of the disease. These images are used to train two deep networks, the Convolution Neural Network (CNN), and the Long Short-Term Memory Network (LSTM) which is an artificial Recurrent Neural Network (RNN). The proposed study involves three phases. First, the CNN model is trained on raw CXR images. Next, it is trained on pre-processed CXR images and finally enhanced CXR images are used for deep network CNN training. Geometric transformations, color transformations, image enhancement, and noise injection techniques are used for augmentation. From augmentation, we get 3,220 augmented CXRs as training datasets. In the final phase, CNN is used to extract the features of CXR imagery that are fed to the LSTM model. The performance of the four trained models is evaluated by the evaluation techniques of different models, including accuracy, specificity, sensitivity, false-positive rate, and receiver operating characteristic (ROC) curve. Results: We compare our results with other benchmark CNN models. Our proposed CNN-LSTM model gives superior accuracy (99.02%) than the other state-of-the-art models. Our method to get improved input, helped the CNN model to produce a very high true positive rate (TPR 1) and no false-negative result whereas false negative was a major problem while using Raw CXR images. Conclusions: We conclude after performing different experiments that some image pre-processing and augmentation, remarkably improves the results of CNN-based models. It will help a better early detection of the disease that will eventually reduce the mortality rate of COVID.

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Evaluation of optimal urine screening and confirmation cut-off values for opiates, at a national reference laboratory

Turkish Journal of Biochemistry ◽

10.1515/tjb-2020-0614 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Çiğdem Karakükcü ◽

Mehmet Zahid Çıracı ◽

Derya Kocer ◽

Mine Yüce Faydalı ◽

Muhittin Abdulkadir Serdar

Keyword(s):

False Positive ◽

False Positive Rate ◽

False Negative ◽

Urine Samples ◽

Reference Laboratory ◽

National Reference Laboratory ◽

Urine Screening ◽

National Reference ◽

Positive Rate ◽

Sensitivity Specificity

Abstract Objectives To obtain optimal immunoassay screening and LC-MS/MS confirmation cut-offs for opiate group tests to reduce false positive (FP) and false negative (FN) rates. Methods A total of 126 urine samples, −50 opiate screening negative, 76 positive according to the threshold of 300 ng/mL by CEDIA method – were confirmed by a full-validated in-house LC-MS/MS method. Sensitivity, specificity, FP, and FN rates were determined at cut-off concentrations of both 300 and 2,000 ng/mL for morphine and codeine, and 10 ng/mL for heroin metabolite 6-mono-acetyl-morphine (6-MAM). Results All CEDIA opiate negative urine samples were negative for morphine, codeine and 6-MAM. Although sensitivity was 100% for each cut-off; specificity was 54.9% at CEDIA cut-off 300 ng/mL vs. LC-MS/MS cut-off 300 ng/mL and, 75% at CEDIA cut-off 2,000 ng/mL vs. LC-MS/MS cut-off 2,000 ng/mL. False positive rate was highest (45.1%) at CEDIA cut-off 300 ng/mL. At CEDIA cut-off 2,000 ng/mL vs. LC-MS/MS cut-off 300 ng/mL, specificity increased to 82.4% and FP rate decreased to 17.6%. All 6-MAM positive samples had CEDIA concentration ≥2,000 ng/mL. Conclusions 2,000 ng/mL for screening and 300 ng/mL for confirmation cut-offs are the most efficient thresholds for the lowest rate of FP opiate results.

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Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

10.36227/techrxiv.12083964 ◽

2020 ◽

Cited By ~ 1

Author(s):

Lawrence Hall ◽

Dmitry Goldgof ◽

Rahul Paul ◽

Gregory M. Goldgof

Keyword(s):

False Negative ◽

True Positive Rate ◽

Small Data ◽

Good Resolution ◽

X Rays ◽

True Positive ◽

Data Set ◽

X Ray ◽

Negative Rate ◽

Positive Rate

Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 135 chest X-rays of COVID-19 and 320 chest X-rays of viral and bacterial pneumonia. A pre-trained deep convolutional neural network, Resnet50 was tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were an overall accuracy of 89.2% with a COVID-19 true positive rate of 0.8039 and an AUC of 0.95. Pre-trained Resnet50 and VGG16 plus our own small CNN were tuned or trained on a balanced set of COVID-19 and pneumonia chest X-rays. An ensemble of the three types of CNN classifiers was applied to a test set of 33 unseen COVID-19 and 218 pneumonia cases. The overall accuracy was 91.24% with the true positive rate for COVID-19 of 0.7879 with 6.88% false positives for a true negative rate of 0.9312 and AUC of 0.94. This preliminary study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images at good resolution will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19.

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Letters to the Editor

PEDIATRICS ◽

10.1542/peds.68.1.144a ◽

1981 ◽

Vol 68 (1) ◽

pp. 144-145

Author(s):

Lachlan Ch De Crespigny ◽

Hugh P. Robinson

Keyword(s):

Real Time ◽

Linear Array ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Letters To The Editor ◽

Premature Babies ◽

Computed Tomography Scans ◽

Positive Rate ◽

Time Linear

We read with interest the report which suggested that the diagnosis of cerebroventricular hemorrhage ([CVH] including both subependymal [SEH] and intraventricular) with real time ultrasound was unreliable.1 Ultrasound, when compared with computed tomography scans, had a 35% false-positive rate and a 21% false-negative rate. In our institution over a 12-month period more than 200 premature babies have been examined (ADR real time linear array scanner with a 7-MHz transducer).

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Pathologic Assessment of Computerized Tomography Accuracy for the Evaluation of the Laryngeal Cartilaginous Frame Work in Laryngopharyngeal Carcinomas

Tumori Journal ◽

10.1177/030089168907500216 ◽

1989 ◽

Vol 75 (2) ◽

pp. 156-162 ◽

Cited By ~ 5

Author(s):

Sandro Sulfaro ◽

Francesco Querin ◽

Luigi Barzan ◽

Mario Lutman ◽

Roberto Comoretto ◽

...

Keyword(s):

Computerized Tomography ◽

False Positive ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Cartilage Destruction ◽

High False Negative Rate ◽

Positive Rate ◽

Pathologic Assessment ◽

Low Sensitivity

Sixty-six whole-organ sectioned laryngopharyngectomy specimens removed for cancer during a seven-year period were uniformly examined to determine the accuracy of preoperative high resolution computerized tomography (CT) for detection of cartilaginous involvement. Our results indicate that CT has a high overall specificity (88.2%) but a low sensitivity (47.1 %); we observed a high false-negative rate (26.5%) and a fairly low false-positive rate (5.9%). Massive cartilage destruction was easily assessed by CT, whereas both small macroscopic and microscopic neoplastic foci of cartilaginous invasion were missed on CT scans. Moreover, false-positive cases were mainly due to proximity of the tumor to the cartilage. Clinical implications of these results are discussed.

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Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing

Biomolecules ◽

10.3390/biom9120809 ◽

2019 ◽

Vol 9 (12) ◽

pp. 809

Author(s):

Miguel Carrasco ◽

Patricio Toledo ◽

Nicole D. Tischler

Keyword(s):

Signal To Noise Ratio ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Keyhole Limpet Hemocyanin ◽

True Positive Rate ◽

Cryo Electron Microscopy ◽

Positive Rate ◽

Top View ◽

Non Negative Matrix Factorization

Segmentation is one of the most important stages in the 3D reconstruction of macromolecule structures in cryo-electron microscopy. Due to the variability of macromolecules and the low signal-to-noise ratio of the structures present, there is no generally satisfactory solution to this process. This work proposes a new unsupervised particle picking and segmentation algorithm based on the composition of two well-known image filters: Anisotropic (Perona–Malik) diffusion and non-negative matrix factorization. This study focused on keyhole limpet hemocyanin (KLH) macromolecules which offer both a top view and a side view. Our proposal was able to detect both types of views and separate them automatically. In our experiments, we used 30 images from the KLH dataset of 680 positive classified regions. The true positive rate was 95.1% for top views and 77.8% for side views. The false negative rate was 14.3%. Although the false positive rate was high at 21.8%, it can be lowered with a supervised classification technique.

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Validity and Reliability of the Hooper Visual Organization Test

Perceptual and Motor Skills ◽

10.2466/pms.1974.39.1.95 ◽

1974 ◽

Vol 39 (1) ◽

pp. 95-100 ◽

Cited By ~ 13

Author(s):

Allan Gerson

Keyword(s):

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Functional Disorders ◽

Validity And Reliability ◽

Clinical Value ◽

Clear Cut ◽

Positive Rate ◽

Organic Brain ◽

Visual Organization

To assess the validity and reliability of the Hooper Visual Organization Test, 68 Ss, of whom 16 were clinically and psychometrically determined to be suffering from organic brain damage, 19 had functional disorders, and 33 were without organic or functional disorders (normal), were given the test. The instrument was shown to be reliable ( r = .80), however, clear-cut discriminations between groups were not achieved. There were significant differences in scores of normal and damaged groups, functional and damaged Ss, but not functional and normal Ss. The qualitative signs said to aid in differentiations were totally absent from all protocols. Performance was affected in part by IQ and other aspects of recognition of meaning. There was a 19% false negative rate for the functionals and a 51% false positive rate for normals. The conclusion was that this device is of dubious clinical value.

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Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/0954411920924496 ◽

2020 ◽

Vol 234 (8) ◽

pp. 794-811

Author(s):

Harikrishna Mulam ◽

Malini Mudigonda

Keyword(s):

Neural Network ◽

Eye Movements ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Principal Component ◽

The Neural Network ◽

Marquardt Algorithm ◽

Positive Rate ◽

Sensitivity Specificity

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human–computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg–Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.

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Screening for Down's Syndrome: The First Two Years Experience in Bristol

Journal of Medical Screening ◽

10.1177/096914139500200407 ◽

1995 ◽

Vol 2 (4) ◽

pp. 207-210 ◽

Cited By ~ 8

Author(s):

D J Goldie ◽

J P Astley ◽

J M Beaman ◽

D A Bickley ◽

A Gunneberg ◽

...

Keyword(s):

False Positive Rate ◽

Down's Syndrome ◽

Down’S Syndrome ◽

District General Hospital ◽

Serum Marker ◽

Serum Markers ◽

Maternal Serum ◽

Age Related ◽

Related Risk ◽

Positive Rate

Objectives – To evaluate the effectiveness of a programme for antenatal screening for Down's syndrome using a fetoprotein and total human chorionic gonadotrophin as maternal serum markers. Setting –A district general hospital providing a screening service to a local purchasing authority and (under contract) to another purchasing authority in the same region. Methods – Patients were counselled and screened between 15 and 20 weeks gestation and Down's risk estimates calculated using the maternal serum marker results as modifiers of the age related risk. Outcome was determined in collaboration with the Regional Cytogenetics Unit. Outcome Measures – Detection rate for Down's syndrome, false positive rate, uptake of screening, and uptake of amniocentesis. Results– In two years 22 816 women were screened (approximately 84% of population); 32 Down's pregnancies were identified, 19 (59.4%) had a reported risk of ≥1:250 and 20 (62.5%) a reported risk of ≥1:300. Of those screened before 17 weeks, 16/20 (80%) had a reported risk of ≥1: 300 compared with 4/12 (33%) of those screened later (P = 0.008); 4.64% of patients screened had reported risks ≥1: 250 and 5.87% reported risks of ≥ 1:300. Amniocentesis uptake was 70% in patients with reported risks of ≥ 1:300. Conclusions –Overall the screening programme was effective but screening before 17 weeks was very much more effective than screening later.

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