Joint Track Machine Learning: An Autonomous Method of Measuring Total Knee Arthroplasty Kinematics From Single-Plane X-Ray Images.

BACKGROUND: Dynamic radiographic measurements of 3-dimensional (3-D) total knee arthroplasty (TKA) kinematics have provided important information for implant design and surgical technique for over 30 years. However, current methods of measuring TKA kinematics are too cumbersome, inaccurate, or time-consuming for practical clinical application. Even state-of-the-art techniques require human-supervision to obtain clinically reliable kinematics. Eliminating human supervision could potentially make this technology practical for clinical use. METHODS: We demonstrate a fully autonomous pipeline for quantifying 3D-TKA kinematics from single-plane radiographic imaging. First, a convolutional neural network (CNN) segmented the femoral and tibial implants from the image. Second, those segmented images were compared to precomputed shape libraries for initial pose estimates. Lastly, a numerical optimization routine aligned 3D implant contours and fluoroscopic images to obtain the final implant poses. RESULTS: The autonomous technique reliably produces kinematic measurements comparable to human-supervised measures, with root-mean-squared differences of less than 0.7 mm and 4° for our test data, and 0.8 mm and 1.7° for external validation studies. CONCLUSION: A fully autonomous method to measure 3D-TKA kinematics from single-plane radiographic images produces results equivalent to a human-supervised method, and may soon make it practical to perform these measurements in a clinical setting.

The use of total reflectance X-ray fluorescence (TXRF) for the determination of metals in the pharmaceutical industry.

The control of residual metals in active pharmaceutical ingredients (API's) and intermediates is critical because of their potential toxic effects. A variety of technologies are available to measure residual metals in pharmaceutical compounds including, AAS, ICP-AES, and ICP-MS. The newest technology is total reflectance X-ray fluorescence spectroscopy (TXRF) which uses primary X-rays to excite atoms which then emit secondary X-rays. The emitted X-rays are characteristic of the individual elements present, and the intensities of the emitted X-rays are proportional to the concentrations of the elements present in the sample. The benefits of TXRF are that it is essentially unaffected by matrix effects, is very sensitive (ppb's), requires small amounts of sample (5-10 mg), and requires very little sample preparation time. During this study, TXRF was used to quantitatively measure residual metals in API's and intermediates and such topics as sample preparation, sensitivity, linearity, reproducibility and accuracy are discussed. The results obtained by TXRF were compared with those obtained by ICP-MS for the same samples for Pd and Cu measurement, and statistical analysis indicated that the results obtained by the two technologies are equivalent at the 95% confidence level. A comparison is also made of the capabilities of the instruments using a tungsten (W) or a molybdenum (Mo) source for excitation. Both instruments could be used for the quantitative determination of residual metals in pharmaceuticals.

Identification of low-level degradants from low dose tablets.

A multifaceted approach was successfully used to identify three of four unknown degradants in degraded low dose tablets. Accelerated solvent extraction (ASE) was found to be an invaluable tool in this multifaceted approach. ASE was capable of extracting four individual degradants of an active pharmaceutical component from 10 tablets into 15 mL of solvent with approximately 100% recovery for each degradant. Using ASE instead of manual extraction led to the extraction and isolation of the degradants in 1 day instead of 7 days. One of the degradants was extracted by ASE, isolated by semi-prep HPLC, and identified by LC-MS and NMR spectroscopy. The structures of two of the remaining three degradants were confirmed by synthesis of authentic samples, while the fourth degradant is yet to be identified.

Pharmaceutical impurity identification: a case study using a multidisciplinary approach.

A multidisciplinary team approach to identify pharmaceutical impurities is presented in this article. It includes a representative example of the methodology. The first step is to analyze the sample by LC-MS. If the structure of the unknown impurity cannot be conclusively determined by LC-MS, LC-NMR is employed. If the sample is unsuitable for LC-NMR, the impurity needs to be isolated for conventional NMR characterization. Although the technique of choice for isolation is preparative HPLC, enrichment is often necessary to improve preparative efficiency. One such technique is solid-phase extraction. For complete verification, synthesis may be necessary to compare spectroscopic characteristics to those observed in the original sample. Although not widely practiced, an effective means of getting valuable structural information is to conduct a degradation study on the purified impurity itself. This systematic strategy was successfully applied to the identification of an impurity in the active pharmaceutical ingredient 1-(1,2,3,5,6,7-hexahydro-s-indacen-4-yl)-3-[4-(1-hydroxy-1-methyl-ethyl)-furan-2-sulphonylurea. Identification required the use of all of the previously mentioned techniques. The instability of the impurity under acidic chromatographic conditions presented an additional challenge to purification and identification. However, we turned this acidic instability to an advantage, conducting a degradation study of the impurity, which provided extensive and useful information about its structure. The following discussion describes how the information gained from each analytical technique was brought together in a complementary fashion to elucidate a final structure.