Medical Devices; Hematology and Pathology Devices; Classification of the Whole Slide Imaging System. Final order

The Food and Drug Administration (FDA or we) is classifying the whole slide imaging system into class II (special controls). The special controls that apply to the device type are identified in this order and will be part of the codified language for the whole slide imaging system's classification. We are taking this action because we have determined that classifying the device into class II (special controls) will provide a reasonable assurance of safety and effectiveness of the device. We believe this action will also enhance patients' access to beneficial innovative devices, in part by reducing regulatory burdens.

Medical Devices; Neurological Devices; Classification of the Computerized Cognitive Assessment Aid for Concussion. Final order

The Food and Drug Administration (FDA) is classifying the Computerized Cognitive Assessment Aid for Concussion into class II (special controls). The special controls that will apply to the device are identified in this order and will be part of the codified language for the computerized cognitive assessment aid for concussion's classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Medical Devices; Neurological Devices; Classification of the Computerized Cognitive Assessment Aid. Final order

The Food and Drug Administration (FDA) is classifying the computerized cognitive assessment aid into class II (special controls). The special controls that will apply to the device are identified in this order, and will be part of the codified language for the computerized cognitive assessment aid's classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Diagnosing scrapie in sheep: A classification experiment

Scrapie is a neuro-degenerative disease in small ruminants. A data set of 3113 records of sheep reported to the Scrapie Notifications Database in Great Britain has been studied. Clinical signs were recorded as present/absent in each animal by veterinary officials (VO) and a post-mortem diagnosis was made. In an attempt to detect healthy animals within the set of suspects using only the clinical signs, 18 classification methods were applied ranging from simple linear classifiers to classifier ensembles such as Bagging, AdaBoost and Random Forests. The results suggest that the clinical classification by the VO was adequate as no further differentiation within the set of suspects was feasible.

Web-based triage in a college health setting

The authors describe the initiation and use of a Web-based triage system in a college health setting. During the first 4 months of implementation, the system recorded 1,290 encounters. More women accessed the system (70%); the average age was 21.8 years. The Web-based triage system advised the majority of students to seek care within 24 hours; however, it recommended self-care management in 22.7% of encounters. Sore throat was the most frequent chief complaint (14.2%). A subset of 59 students received treatment at student health services after requesting an appointment via e-mail. The authors used kappa statistics to compare congruence between chief complaint and 24/7 WebMed classification (kappa = .94), between chief complaint and student health center diagnosis (kappa = .91), and between 24/7 WebMed classification and student health center diagnosis (kappa = .89). Initial evaluation showed high use and good accuracy of Web-based triage. This service provides education and advice to students about their health care concerns.

Comparison of machine learning and traditional classifiers in glaucoma diagnosis

Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visual-field sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visual-field test whose output is amenable to machine learning. We compared the performance of a number of machine learning algorithms with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation. The machine learning algorithms studied included multilayer perceptron (MLP), support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG). MLP and SVM are classifiers that work directly on the decision boundary and fall under the discriminative paradigm. Generative classifiers, which first model the data probability density and then perform classification via Bayes' rule, usually give deeper insight into the structure of the data space. We have applied MOG, MGG, LDA, QDA, and Parzen window to the classification of glaucoma from SAP. Performance of the various classifiers was compared by the areas under their receiver operating characteristic curves and by sensitivities (true-positive rates) at chosen specificities (true-negative rates). The machine-learning-type classifiers showed improved performance over the best indexes from STATPAC. Forward-selection and backward-elimination methodology further improved the classification rate and also has the potential to reduce testing time by diminishing the number of visual-field location measurements.

Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry

PURPOSE

To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma.

METHODS

Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained and tested by cross validation on the numerical plot of absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes, designated as such by the appearance of the optic nerve. The authors compared performance of these classifiers with the global indices of STATPAC, using the area under the ROC curve. Two human experts were judged against the machine classifiers and the global indices by plotting their sensitivity-specificity pairs.

RESULTS

MoG had the greatest area under the ROC curve of the machine classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest areas under the curve of the global indices. MoG had significantly greater ROC area than PSD and CPSD. Human experts were not better at classifying visual fields than the machine classifiers or the global indices.

CONCLUSIONS

MoG, using the entire visual field and age for input, interpreted SAP better than the global indices of STATPAC. Machine classifiers may augment the global indices of STATPAC.

Computer-aided detection and diagnosis of breast cancer

The limitations of radiologists when interpreting mammogram examinations provides a reasonable, if not compelling, basis for application of computer techniques that have the potential to improve diagnostic performance. Computer algorithms, at their present state of development, show great promise for clinical use. It can be expected that such use will only improve as computer technology and computer methods continue to become more formidable. The eventual role of computers in mammographic detection and diagnosis has not been fully defined, but their effect on practice may one day be very significant.

Spectroscopic diagnosis of esophageal cancer: new classification model, improved measurement system

Laser-induced fluorescence spectroscopy was used to measure fluorescence emission of normal and malignant tissue during endoscopy in patients with esophageal cancer and volunteers with normal esophagus. The spectroscopy system consisted of a nitrogen-pumped dye-laser tuned at 410 nm for excitation source, an optical multichannel analyzer for spectrum analysis, and a fiberoptic probe designed for both the delivery of excitation light and the collection of fluorescence emission from tissue. The fluorescence lineshape of each spectrum was determined and sampled at 15-nm intervals from 430 to 716 nm. A calibration set of spectra from normal and malignant spectra was selected. Using stepwise discriminate analysis, significant wavelengths that separated normal from malignant spectra were selected. The intensities at these wavelengths were used to formulate a classification model using linear discriminate analysis. The model was then used to classify additional tissue spectra from 26 malignant and 108 normal sites into either normal or malignant spectra. A sensitivity of 100% and specificity of 98% were obtained.

Two-level classification schemes in medical diagnostics

A two-level classifier for medical applications is considered. Such classifiers are expected to yield a more precise result than classical one-level classifiers. The underlying idea for two-level classification is supported by the routine practice of physicians to confirm the diagnosis by several data-driven inferences. An overview of the types of the two-level classifiers is presented. The competitive two-level classifier is emphasized. Three examples with real clinical data are presented from the fields of cardiology, aviation medicine, and neonatology.