This pilot study has applied machine learning (artificial intelligence derived qualitative analysis procedures) to yield non-invasive techniques for the assessment and interpretation of clinical and laboratory data in glomerular disease. To evaluate the appropriateness of these techniques, they were applied to subsets of a small database of 284 case histories and the resulting procedures evaluated against the remaining cases. Over such evaluations, the following average diagnostic accuracies were obtained: microscopic polyarteritis, 95.37%; minimal lesion nephrotic syndrome, 96.50%; immunoglobulin A nephropathy, 81.26%; minor changes, 93.66%; lupus nephritis, 96.27%; focal glomerulosclerosis, 92.06%; mesangial proliferative glomerulonephritis, 92.56%; and membranous nephropathy, 92.56%. Although in general the new diagnostic system is not yet as accurate as the histological evaluation of renal biopsy specimens, it shows promise of adding a further dimension to the diagnostic process. When the machine learning techniques are applied to a larger database, greater diagnostic accuracy should be obtained. It may allow accurate non-invasive diagnosis of some cases of glomerular disease without the need for renal biopsy. This may reduce both the cost and the morbidity of the investigation of glomerular disease and may be of particular value in situations where renal biopsy is considered hazardous or contraindicated.
|Number of pages||7|
|Journal||Nephrology Dialysis Transplantation|
|Publication status||Published - 1 Jan 1992|
- Glomerular disease
- Histological classification
- Machine learning